Functional analysis of genetic variants associated …...I declare that this thesis is a...

DISSERTATION

ZUR ERLANGUNG DES DOKTORGRADES

DER NATURWISSENSCHAFTEN (DR. RER. NAT.)

DER FAKULTÄT FÜR BIOLOGIE UND VORKLINISCHE MEDIZIN

DER UNIVERSITÄT REGENSBURG

vorgelegt von Shyamtanu Datta

aus INDIA

DECEMBER 2015

Functional analysis of genetic variants

associated with age-related macular

degeneration (AMD) - The HtrA serine

peptidase 1 (HTRA1)

Der Promotionsgesuch wurde eingereicht am:

Die Arbeit wurde angeleitet von:

Prof. Dr. Bernhard Weber

Unterschrift:

SHYAMTANU DATTA

To my parents and Gurudev

“The only thing worse than being blind is having sight but no vision.”

Hellen Keller (1880-1968)

DECLARATION

I declare that this thesis is a presentation of my original research work. Wherever

contributions of others are involved, every effort is made to indicate this clearly, with

due reference to the literature, acknowledgement of collaborative research and

discussions. This thesis has not been submitted to any other faculty or university for

any kind of examination and a part of the thesis is composed of the following

published article:

Friedrich, U.*, Datta, S.*, Schubert, T., Plossl, K., Schneider, M., Grassmann, F., Fuchshofer, R., Tiefenbach, K.J., Langst, G., and Weber, B.H. (2015). Synonymous variants in HTRA1 implicated in AMD susceptibility impair its capacity to regulate TGF-beta signaling. Hum Mol Genet 24, 6361-6373.

(* indicates shared first authorship)

I have given all information truthfully to the best of my knowledge.

................................................ ...................................................... Date Signature

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Table of Contents

SUMMARY .................................................................................................................. 1

1. INTRODUCTION ..................................................................................................... 3

1.1. Age-related Macular Degeneration (AMD) .....................................................................3

1.1.1. Anatomy of healthy human retina.....................................................................................3

1.1.2. Pathology and pathogenesis of AMD ...............................................................................5

1.1.2.1. Role of microglia in AMD pathogenesis ........................................................................8

1.1.3. Etiology of AMD ............................................................................................................10

1.1.3.1. Aging ............................................................................................................................10

1.1.3.2. Environmental factors ..................................................................................................11

1.1.3.3. Race/ethnicity ...............................................................................................................11

1.1.3.4. Genetic factors .............................................................................................................12

1.1.3.4.1. The 1q31 locus ..................................................................................................................... 12

1.1.3.4.2. The 10q26 locus ................................................................................................................... 13

1.1.3.4.3. Other AMD-associated gene loci ......................................................................................... 15

1.2. HTRA1 protein.................................................................................................................16

1.2.1. Structure of HTRA1 ........................................................................................................16

1.2.2. Function of HTRA1 ........................................................................................................18

2. AIM OF THE STUDY ............................................................................................. 19

3. MATERIALS AND METHODS .............................................................................. 20

3.1. Materials ...........................................................................................................................20

3.1.1. Chemicals and reagents...................................................................................................20

3.1.2. Kits and ready-made solutions ........................................................................................22

3.1.3. Buffers and solutions ......................................................................................................24

3.1.4. Cell lines .........................................................................................................................26

3.1.4.1. Bacterial cells ..............................................................................................................26

3.1.4.2 Mammalian cell lines ....................................................................................................26

3.1.4.3. Media and supplements................................................................................................27

3.1.5. Enzymes ..........................................................................................................................27

3.1.6. Antibodies .......................................................................................................................28

3.1.7. Vectors and plasmids ......................................................................................................29

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3.1.8. Oligonucleotides .............................................................................................................30

3.1.9. Consumables ...................................................................................................................32

3.1.10. Instruments ....................................................................................................................33

3.1.11. Software tools ...............................................................................................................36

3.2. Methods .............................................................................................................................37

3.2.1. Cultivation of mammalian cell lines ...............................................................................37

3.2.2. Cultivation of E.coli ........................................................................................................38

3.2.3. Cloning strategy ..............................................................................................................38

3.2.3.1. Amplification of DNA fragments ..................................................................................38

3.2.3.2. Agarose gel electrophoresis .........................................................................................40

3.2.3.3. DNA extraction from agarose gels ..............................................................................41

3.2.3.4. Determination of DNA concentrations ........................................................................41

3.2.3.5. DpnI digestion ..............................................................................................................41

3.2.3.6. A-tailing of blunt-ended PCR fragments......................................................................42

3.2.3.7. Ligation into pGEM®-T vector ....................................................................................42

3.2.3.8. Heat shock transformation of competent E.coli cells ..................................................42

3.2.3.9. Selection of positive clones ..........................................................................................43

3.2.3.10. Plasmid isolation .......................................................................................................43

3.2.3.11. Cycle sequencing of inserts ........................................................................................44

3.2.3.12. Restriction digestion of correct inserts and ligation into target vectors ...................44

3.2.3.13. Long-term storage of positive clones .........................................................................46

3.2.4. Transfection of Hek293-Ebna cell lines .........................................................................46

3.2.5. Secretion assay ................................................................................................................46

3.2.6. Preparation of protein samples for gel loading ...............................................................47

3.2.7. Bradford assay for measurement of protein concentration .............................................47

3.2.8. SDS PAGE (Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis) ................48

3.2.9. Western blot (WB)/Immunoblot (IB) .............................................................................48

3.2.10. MicroScale Thermophoresis (MST) to study conformation of HTRA1 isoforms ........49

3.2.10.1. In-Gel TC-tagged HTRA1 detection ..........................................................................49

3.2.10.2. The temperature-dependent structural assays by MST ..............................................50

3.2.11. Purification of Strep-tagged variants of the HTRA1 ....................................................50

3.2.12. Coomassie staining .......................................................................................................51

3.2.13. Casein digest to test bio-activity of HTRA1 protein ....................................................51

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3.2.14. Limited partial proteolysis ............................................................................................51

3.2.15. MST interaction analysis ..............................................................................................51

3.2.16. TGF-β1/β-casein in vitro digestion ...............................................................................52

3.2.17. MLEC luciferase assay .................................................................................................52

3.2.18. Treatment of BV-2 cells with BV-2-conditioned medium and HTRA1 .......................53

3.2.19. BV-2 cells treatment with lipopolysaccharide (LPS) and HTRA1 ...............................55

3.2.20. BV-2 cells treatment with interleukin 4 (IL4), TGF-β1 and HTRA1 ...........................55

3.2.21. Nitrite measurement by nitric oxide (NO) assay ..........................................................55

3.2.22. RNA analysis ................................................................................................................56

3.2.22.1. RNA isolation from cell cultures ................................................................................56

3.2.22.2. First strand cDNA synthesis from RNA .....................................................................56

3.2.22.3. qRT-PCR ....................................................................................................................57

4. RESULTS .............................................................................................................. 58

4.1. Cloning and expression of HTRA1 variants ..................................................................58

4.1.1. HTRA1 haplotypes applied in subsequent studies ..........................................................58

4.1.2. HTRA1 expression constructs .........................................................................................58

4.1.3. Characterization of HTRA1 expression constructs .........................................................59

4.2 Influence of synonymous SNPs within HTRA1 exon 1 on protein structure ...............61

4.2.1. Preparation and adjustment of TC-tagged HTRA1 isoforms .........................................61

4.2.2. Labeling TC-tagged HTRA1 with FlAsH-EDT2 for MST analyses ...............................62

4.2.3. HTRA1:CG, HTRA1:TT and HTRA1:CC protein conformation comparison by MST 63

4.3. HTRA1:CG, HTRA1:TT and HTRA1:CC protein conformation comparison by

limited partial proteolysis.......................................................................................................63

4.4. Influence of synonymous SNPs within HTRA1 exon 1 on protein secretion ..............65

4.5. Influence of synonymous SNPs within HTRA1 exon 1 on its substrate affinity .........66

4.5.1. Interaction of HTRA1 isoforms with TGF-β and β-casein analyzed by MST ...............66

4.5.2. Proteolytic cleavage of TGF-β and β-casein by different HTRA1 isoforms ..................67

4.6. Effect of HTRA1:CG and HTRA1:TT on TGF-β signaling ........................................69

4.6.1. Effect of HTRA1:CG and HTRA1:TT on TGF-β1-induced PAI-1 promoter activity in

MLEC-PAI/Luc cells. ...............................................................................................................69

4.6.2. Effect of HTRA1:CG and HTRA1:TT on SMAD phosphorylation ...............................70

4.6.3. Effect of HTRA1:CG and HTRA1:TT on relative Pai-1 gene expression .....................72

4.7. Effect of HTRA1 on microglial activation .....................................................................73

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4.7.1. Effect of HTRA1 on classical activation of microglial (BV-2) cells via LPS treatment 73

4.7.2. Effect of HTRA1 on alternative activation of microglial (BV-2) cells via IL4 and TGF-

β1 treatment ..............................................................................................................................75

5. DISCUSSION ........................................................................................................ 78

5.1. Effect of synonymous polymorphisms within exon 1 of HTRA1 on its structure and

secretion ...................................................................................................................................78

5.2. Effect of synonymous polymorphisms within exon 1 of HTRA1 on its substrate

specificity .................................................................................................................................80

5.3. Effect of HTRA1 variants on TGF-β signaling .............................................................83

5.4. Effect of HTRA1 on classical and alternative microglial activation ...........................85

6. CONCLUSION ...................................................................................................... 88

LIST OF FIGURES .................................................................................................... 89

LIST OF TABLES...................................................................................................... 91

ABBREVIATIONS..................................................................................................... 92

REFERENCES .......................................................................................................... 95

ACKNOWLEDGMENT ............................................................................................ 121

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SUMMARY

Background. Age-related macular degeneration (AMD) is a multifactorial retinal

neurodegenerative disorder and the leading cause of blindness among the elderly

worldwide. Genetics is one of several factors which play role in the pathogenesis. An

AMD-risk locus on chromosome 10q26 spans two genes namely age-related

maculopathy susceptibility 2 (ARMS2) and high temperature requirement factor A1

(HTRA1). Controversy exists as to which of the two genes are responsible for

increased risk of the disease. HTRA1 is a secreted serine protease reported to play a

crucial role in the development of several cancers and neurodegenerative diseases.

ARMS2 is a primate-specific gene and, so far, biological properties attributed to the

putative protein remain elusive. Two synonymous single nucleotide polymorphisms

(SNPs) in exon 1 of HTRA1 are in complete linkage disequilibrium with several

polymorphisms within 10q26 locus which are strongly associated with AMD.

Aim. The aim of the study was to assess an effect of AMD-associated synonymous

SNPs on the structure and function of HTRA1. In addition, a putative role of HTRA1

on activation of microglia was investigated.

Methods. Differences in the structures of recombinant non-risk- and risk-associated

HTRA1 isoforms were analyzed by MicroScale Thermophoresis (MST) and limited

partial proteolysis. The secretion of the different HTRA1 isoforms was analyzed after

heterologous expression in Hek293-Ebna cells. By employing MST and in vitro

digestion assays, the interaction of HTRA1 isoforms with reported interaction

partners, transforming growth factor-β1 (TGF-β1) and β-casein, was compared.

Luciferase assays were applied to compare the regulation of TGF-β1 signaling by

different HTRA1 isoforms in MLEC-PAI/Luc cells (Mink lung epithelial cells stably

transfected with an expression construct containing a truncated PAI-1 promoter fused

to the firefly luciferase reporter gene). The influence of HTRA1 non-risk- and risk-

associated isoforms was also analyzed on autocrine TGF-β signaling in BV-2

microglial cells addressing protein and transcript levels. Finally, an effect of HTRA1

on classical (M1) and alternative (M2) activation of microglia was assessed by

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treating BV-2 cells with known stimulators for both pathways in presence of purified

recombinant HTRA1. The gene expression of markers of M1 and M2 activation as

well as nitrite production by M1 microglial cells were investigated.

Results. MST and limited partial proteolysis showed that the conformation of the

AMD risk-associated HTRA1 protein is different from that of the non-risk-associated

HTRA1 isoforms. The risk-associated isoform was also found to have decreased

secretion. While there was no difference of the HTRA1 isoforms in casein binding

and digestion, the risk-associated isoform exhibited no binding and decreased

digestion of TGF-β1. This eventually affected the regulation of TGF-β signaling in

MLEC-PAI/Luc cells and microglial cells. In addition, preliminary data indicate that

HTRA1 might be a regulator of M2 microglial activation induced by IL4 and TGF-β1.

Nevertheless, more experiments are required to support the role of HTRA1 on

microglial activation.

Conclusion. Our data show an effect of AMD-associated synonymous polymorph-

isms on HTRA1 secretion and protein structure, thereby affecting the capacity of

HTRA1 to regulate TGF-β signaling. Whether this plays a role in pathogenesis of

AMD still remains to be clarified.

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1. INTRODUCTION

1.1. Age-related Macular Degeneration (AMD)

Age-related macular degeneration (AMD) is one of the leading causes of irreversible

blindness of elderly population worldwide (Resnikoff et al., 2004). First, the condition

was described in medical literature as “symmetrical central choroido-retinal disease

occurring in senile persons” in 1874 (Hutchinson et al., 1874). AMD is a multifactorial

neurodegenerative disease characterized by progressive degeneration of

photoreceptors/retinal pigment epithelial (RPE) complex primarily in macular region

of the retina, which has the highest concentration of cone photoreceptors and is

responsible for visual acuity (Curcio et al., 1990; Smith et al., 2001; Klein et al.,

2010). It is a complex disease owing to multiple risk factors such as age, diet,

smoking, oxidative stress and genetics (Klein et al., 2010). The disease is associated

not only with visual impairment but also with high rates of depression, anxiety and

emotional distress (Berman and Brodaty, 2006). A recent meta-analysis has shown

that 8.7% of the worldwide population has AMD, and the projected number of people

with the disease is around 196 million in 2020, increasing to 288 million in 2040

(Wong et al., 2014).

1.1.1. Anatomy of healthy human retina

The human retina is a light-sensitive layer lining the inside of the posterior segment

of the eye (Yanoff et al., 2009). It consists of two distinct layers: 1) the neuroretina

consisting of photoreceptor cells, neuronal cells and glial cells; and 2) the RPE

separated from the neuroretina by a virtual subretinal space. When light entering the

eye is focused on the retina, the neuroretina serves to convert light into neural

signals. A complex neural circuitry within the retina relays these signals to visual

centres in the brain (Luo et al., 2008). Retinal photoreceptors are metabolically

active neurons with oxygen requirements that are among the highest in the human

body (Wong-Riley, 2010). The outer segments of photoreceptors comprise stacks of

flattened membranous discs containing light-sensitive photopigments (rhodopsin in

rods and spectrally tuned red-, green- or blue-sensitive opsins in cones). The macula

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(Latin for “spot”) is a distinct feature of human and most non-human primate retina

and lies in the central visual axis. The human macula (about 6 mm in diameter)

contains a cone-dominated fovea (0.8 mm in diameter) that is associated with high-

acuity vision (Curcio et al., 1990) (Figure 1A and B).

Figure 1: Anatomy of retina. (A) Anatomical depiction of the human eye viewed in sagittal section; (B) Fundus photograph of the retina of a healthy individual, using an ophthalmoscope; (C) Confocal microscopy image of a human retina labeled with fluorescent probes. Cell nuclei in the outer nuclear layer of photoreceptors are indicated by red, photoreceptor outer segments are green and structures containing high concentrations of filamentous actin (cell–cell junctions and vessel walls) are blue. Adapted and modified from Fritsche et al. (2014).

The RPE, which constitutes the outer blood-retinal barrier (BRB) (Bernstein and

Hollenberg, 1965), facilitates photoreceptor turnover by phagocytosis and lysosomal

degradation of outer segments following shedding (Young, 1969; LaVail, 1983).

Moreover, the RPE serves as an excellent support system to the neuroretina, by

delivery of oxygen and metabolites to the photoreceptors, preventing extracellular

fluid leaking into the subretinal space from the underlying choriocapillaris (a

continuous layer of fenestrated capillaries), actively pumping fluid out of the

subretinal space and regulating trafficking of immune cells across the BRB (Forrester

and Xu, 2012). The inner aspect of the choroid, next to the RPE, is Bruch’s

Membrane (BrM), a laminar extracellular matrix (ECM) of collagen and elastin

(Curcio et al., 2013). BrM works as a supply chain by transporting oxygen, glucose

and other metabolites to RPE and photoreceptors, and returns metabolic wastes to

systemic circulation (Booij et al., 2010). Integrity of the BrM appears to be important

in suppressing invasion of vessels from the choroidal circulation into the retina

(Chong et al., 2005). The choriocapillaris arises from the branches of posterior ciliary

arteries and supplies the photoreceptor-RPE complex and outer neuroretina, while

the inner parts of the neuroretina are supplied by the central retinal artery (Bernstein

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and Hollenberg, 1965). Figure 1C shows the order of photoreceptors, RPE, Bruch’s

membrane and choroidal blood vessels in a confocal microscopy image of a human

retinal section.

1.1.2. Pathology and pathogenesis of AMD

A general hallmark of aging retina is the accumulation of lipofuscin in the lysosomal

compartments and the cytoplasm of RPE cells. Lipofuscin are autofluorescent

granules that are remnants of retinoid metabolites from shed photoreceptor outer-

segment membranes. A2E, an abundant component of lipofuscin (Delori et al., 2001;

Strauss, 2005) is hypothesized to exacerbate AMD through photo-oxidation and

phototoxicity in RPE cells (Sparrow and Boulton, 2005). The photo-oxidation could

serve as a trigger for activation of the complement system, a trigger that would

thereby predispose the macula to disease and contribute to the chronic inflammatory

processes observed in AMD pathogenesis. Furthermore, the excessive accumulation

of lipofuscin and the consequent activation of the complement cascade might, over

time, also contribute to the formation of drusen (Zhou et al., 2006; Zhou et al., 2009).

Drusen are cellular debris at the interface between RPE and BrM (or between RPE

and neuroretina). Drusen are classified as small (<63 μm in diameter with discrete

margins), medium (63–124 μm) or large (>125 μm with indistinct edges) (Ferris et

al., 2005). In a recent study, using a combination of high-resolution analytical

techniques, tiny hydroxyapatite (bone mineral) spherules with cholesterol-containing

cores are found in all examined drusen, which may be responsible for initiation of

drusen formation (Thompson et al., 2015). Drusen components include lipids and an

array of proteins, including those involved in complement regulation, Tissue Inhibitor

of Matrixmetalloproteases-3 (TIMP3), vitronectin, β-amyloid and apolipoproteins (E,

B, A-I, C-I and C-II), plus zinc and iron ions (Arnhold et al., 1998; Mullins et al., 2000;

Crabb et al., 2002). AMD patients display a broad spectrum of clinical characteristics

based upon drusen size and AMD pigmentary abnormalities, both hypopigmentation

and hyperpigmentation. Clinical examination of human retinas can reveal distinct

hallmarks of AMD that can be broadly divided into early/intermediate and late

(advanced) stages. Age-Related Eye Disease Study (AREDS) has developed a five-

step severity scale to define risk categories for development of advanced AMD. Early

or intermediate AMD (AREDS grades 2 and 3) is the most common and least severe

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form. Late AMD (AREDS grades 4 and 5) is usually subdivided into dry [geographic

atrophy (GA)] and wet [choroidal neovascularization (CNV)] (Ferris et al., 2005).

These pathological hallmarks of early AMD and late AMD (GA and CNV) are shown

in Figure 2. The AREDS grades and their characteristics are listed in Table 1.

Figure 2: Pathological hallmarks of early AMD and late AMD revealed by fundus photography. (A) Eye with drusen: visible as small yellow spots; (B) wet AMD: eye with choroidal neovascularization and exudation; (C) dry AMD: eye with advanced geographic atrophy. Adapted from Ratnapriya and Chew (2013).

Table 1: Stages of AMD divided into AREDS categories according to

characteristics

AREDS

Category

Stages of AMD Characteristics

Category 1 No AMD A few small or no drusen

Category 2 Early Stage

AMD

Several small drusen or a few medium-sized drusen in one or both eyes

Category 3 Intermediate AMD

Many medium-sized drusen or one or more large drusen in one or both eyes

Category 4 and

5

Advanced AMD In one eye only, either a break-down of light-sensitive cells and supporting tissue in the central retinal area (advanced dry form), or abnormal and fragile blood vessels under the retina (wet form)

To date, although no cure of dry AMD is available, the only available therapy for

management of dry AMD is daily intake of antioxidant formulation (Age-Related Eye

Disease Study Research, 2001; Age-Related Eye Disease Study 2 Research, 2013).

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According to the first AREDS, combination of the following different antioxidants was

administered: vitamin C (500 mg), vitamin E (400 international units [IU]), beta-

carotene (15 mg), zinc (80 mg) and copper (2 mg). AREDS2 study suggested that

lutein/zeaxanthin could be more appropriate than beta carotene in the AREDS-type

supplements. Other promising antioxidants for dry AMD therapy include crocetin

(Maccarone et al., 2008), curcumin (Mandal et al., 2009; Chang et al., 2014),

Resveratrol (King et al., 2005; Nagineni et al., 2014), and vitamins B9, B12 and B6

(Christen et al., 2009). Besides antioxidants, new findings about pathogenesis of

AMD have led to several potential therapeutic strategies (Buschini et al., 2015). Both

AMD drusen and Alzheimer’s disease plaques contain amyloid beta (Aβ) plaques,

which are in strong association with activated complement components (Johnson et

al., 2002; Ohno-Matsui, 2011). Humanized monoclonal antibodies: RN6G (Pfizer,

New York, NY, USA) and GSK933776 (GlaxoSmithKline, Verona, Italy) (Ding et al.,

2008; Ding et al., 2011) and a drug named glatiramer acetate (Butovsky et al.,

2006a; Landa et al., 2008), directed against Aβ plaques, have been demonstrated to

reduce drusen in AMD mice model and are undergoing clinical trials. Another group

of drugs (fenretinide, ACU-4429 ALK-001) which showed reduction in accumulation

of A2E and lipofuscin also presents an exciting possibility for treating AMD (Mata et

al., 2013; Holz et al., 2014). Development of stem cell therapy for treatment of dry

AMD is another promising strategy (Brandl et al., 2015). Development of surgical

procedures to transplant human embryonic stem cells, which are able to differentiate

into RPE cells, is currently under clinical trials (Carr et al., 2013).

The wet form, also known as neovascular or exudative AMD, constitutes only 10–

15% of all AMD cases, but accounts for at least 80% of AMD-related blindness

(Scholl et al., 2009). In this case, accelerated and profound visual loss typically

occurs as a result of CNV, where new vessels grow and invade the retina resulting in

sub-RPE or subretinal haemorrhages, or fluid accumulation in or below the layers of

the retina (Ferris et al., 1984). In contrast to the typical slow progression of GA, CNV

can decrease vision acutely through the abrupt onset of edema and bleeding from

new capillaries invading the RPE and neural retina. Vascular endothelial growth

factor (VEGF) has been implicated as a key mediator in the pathogenesis of CNV

(Kvanta et al., 1996; Barouch and Miller, 2004; Adamis and Shima, 2005). CNV can

be treated but not be cured with intraocular anti-VEGF antibodies (Ferris, 2004;

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Gragoudas et al., 2004; Rosenfeld et al., 2011). Two most widely used monoclonal

antibodies against VEGF are bevacizumab (Avastin™; Genentech, South San

Francisco, CA, USA), a full-length, humanized monoclonal antibody against VEGF;

and ranibizumab (Lucentis™; Genentech, South San Francisco, CA, USA), a

recombinant, humanized, monoclonal antibody fragment directed toward all isoforms

of VEGF-A (Presta et al., 1997; Chen et al., 1999; Ferrara et al., 2004). These

antibodies bind and neutralize all the biologically active forms of VEGF and thereby

inhibit angiogenesis. Aflibercept (VEGF trap-eye), a fusion protein consisting of key

domains of the human VEGF1 and VEGF2 receptors coupled to the Fc part of a

human IgG molecule, has been approved recently for neovascular AMD (Grisanti et.

al, 2013). It is suggested by a theoretical model that Aflibercept may have a longer

duration of action compared with other treatments (Ohr and Kaiser, 2012).

1.1.2.1. Role of microglia in AMD pathogenesis

Microglial cells are specialized immune cells that reside in the brain and retina, and

are responsible for the initial detection of noxious stimuli arising in the local micro-

environment (Kettenmann et al., 2011). Microglia are functionally different from

blood-derived macrophages, which originate from bone marrow-derived monocytes

and enter the CNS and retina during pathological conditions. Microglia are believed

to originate from macrophages produced by primitive haematopoiesis in the yolk sac

(Alliot et al., 1999). Morphologically, microglia are very adaptable, changing their

phenotype depending on location and role (Gehrmann et al., 1995).

In the healthy retina, microglia are distributed throughout the inner and outer

plexiform layers, where they carry out constant and dynamic surveillance of the

extracellular microenvironment (Lee et al., 2008). In the resting state, they

continuously scan the local environment and phagocytose cell debris (Figure 3A).

Any detection of signs for challenges in the retina, leading to dysfunctions or

degenerations in the RPE, photoreceptor layer and the ganglion cell, rapidly alert the

microglia (Figure 3B). In the effector phase, microglia migrate to the lesion sites,

accumulate in the nuclear layers and the subretinal space and subsequently turn into

amoeboid phagocytes (Figure 3C).

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This complex, multistage activation process converts the “resting microglial cells”

into the “activated microglial cell” (Kettenmann et al., 2011). Microglia exhibit

different states of activation, termed as proinflammatory M1 activation which includes

“classical activation”, and anti-inflammatory M2 activation which includes “alternative

activation” and “acquired deactivation”, depending on the milieu in which they

become activated and the factors they are stimulated by (Le et al., 2001; Li et al.,

2004; Block et al., 2007).

Figure 3: Schematic representation of three common phases of microglial activity in the retina. (A) Resting phase (B) Activation phase (C) Effector phase. Pink cell bodies indicate microglia; yellow stars: insults or injury; RPE: retinal pigment epithelium; ONL: outer nuclear layer; OPL: outer plexiform layer; INL: inner nuclear layer; IPL: inner plexiform layer; GCL: ganglion cell layer. Adapted and modified from Karlstetter et al. (2010).

Microglial cell bodies, typically situated in the inner retina in the normal human eye

(Combadiere et al., 2007) can migrate to the subretinal space (the potential space

between the photoreceptor outer segments and the apical surface of the RPE cells)

in response to inflammatory stimuli (Gupta et al., 2003; Xu et al., 2009). Activated

microglia have been found in the outer retina and subretinal space in eyes with AMD

(Gupta et al., 2003; Combadiere et al., 2007). Retinal microglia express the

chemokine receptor CX3CR1. Genetic polymorphisms in the CX3CR1 gene have

been associated with AMD and reduce the chemotactic ability of monocytes

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(Combadiere et al., 2007). In CX3CR1-deficient mice, accumulation of microglia and

macrophages in the subretinal space has been observed, contributing to drusen

formation and photoreceptors degeneration (Combadiere et al., 2007; Sennlaub et

al., 2013).

1.1.3. Etiology of AMD

Based on epidemiology and pathobiology, prevalence of AMD phenotypes depends

on several risk factors: aging, environmental factors, demographic factors and

genetic factors (Leveziel et al., 2011; Ratnapriya and Chew, 2013; Fritsche et al.,

2014).

1.1.3.1. Aging

As numerous population-based studies indicate, AMD is particularly prevalent in

people who are 60 years and older (Mitchell et al., 1995; Klein et al., 1999a; Klaver

et al., 2001; Wong et al., 2008; Wong et al., 2014). Though aging might not be

enough to trigger AMD single-handedly, along with genetic and environmental risk

factors the pathological changes of AMD become more likely. Aging-associated

expression changes in genes associated with mitochondrial function, protein

metabolism and immune response have been identified in several tissues (Zahn et

al., 2007) and are consistent with some proposed mechanisms for late-onset

neurodegenerative diseases (Wright et al., 2004; Lin and Beal, 2006; Rubinsztein,

2006; Morimoto, 2008). A comparison of young and aging human retinas identified

differential expression of a small number of genes involved in stress and immune

response, protein and energy metabolism, and inflammation (Yoshida et al., 2002).

According to another study, advanced age accounts for 30% of rod photoreceptor

loss in the central macula (Curcio et al., 2000). Moreover, increasing amounts of

A2E in lipofuscin as well as increasing thickness of BrM are also age-related ocular

changes (Feeney-Burns and Ellersieck, 1985) that are potential triggers for AMD

onset (Spraul et al., 1996). Although association of BrM thickness with AMD is

debated (Chong et al., 2005), accumulation of lipoproteins has been found to be a

significant age-related change (Curcio et al., 2001).

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1.1.3.2. Environmental factors

Like all multifactorial complex diseases, environmental factors play an important role

in AMD development. There are increasing number of evidences where smoking has

been consistently associated with AMD, leading to a two-fold or greater risk of

developing the disease when compared to non-smokers (Vingerling et al., 1996;

Age-Related Eye Disease Study Research, 2000; McCarty et al., 2001; Nakayama et

al., 2014; Gopinath et al., 2015; Wu et al., 2015). The mechanism by which smoking

affects the retina is unknown; it has been proposed that smoking may alter the

metabolism of the RPE by interfering with antioxidants and altering choroidal blood

flow (Hawkins et al., 1999). Another independent report also supports that smoking

causes oxidative insults to the retina (Espinosa-Heidmann et al., 2006).

Total fat intake was positively associated with risk of AMD, which may have been

due to intake of individual fatty acids, such as linolenic acid, rather than to total fat

intake per se (Cho et al., 2001). High fatty diet, obesity and other risk factors for

cardiovascular diseases correlate with higher AMD susceptibility. Although the exact

mechanism is not known, it is assumed that the contribution to AMD pathogenesis is

related to increased oxidative stress, changes in the lipoprotein profile and increased

inflammation resulting in increased cellular damage (Katta et al., 2009). Diet high in

antioxidants lutein and zeaxanthin (found mostly in green leafy vegetables) and in

omega-3 fatty acids (primarily found in fish) have been linked to a decreased risk of

neovascular AMD (Flood et al., 2002; Seddon et al., 2003; Weikel et al., 2012).

Apart from diet and smoking, there are additional environmental factors that may

influence AMD pathogenesis, including sunlight exposure (Cruickshanks et al., 2001;

Delcourt et al., 2001; Khan et al., 2006), alcohol use (Moss et al., 1998; Cho et al.,

2000; Chong et al., 2008) and infection by bacterial pathogens (particularly

Chlamydia pneumoniae) (Kalayoglu et al., 2003; Baird et al., 2008).

1.1.3.3. Race/ethnicity

In 2006, an analysis of U.S. participants in the Multiethnic Study of Atherosclerosis

(MESA) showed prevalence of AMD in persons aged 45 to 85 years to be 2.4% in

African-Americans, 4.2% in Hispanics, 4.6% in Chinese-descent individuals and

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5.4% in Caucasians (Klein et al., 2006). In a previous study by National Health and

Nutritional Examination Survey it was observed that prevalence of AMD was 5.1% in

Mexican-Americans, 7.3% in whites, and 2.4% in blacks (Klein et al., 1999b). These

differences could be due to either environmental or genetic factors. It has been

suggested that melanin may protect against the formation of lipofuscin (Weiter et al.,

1986). In 2013, an updated analysis of prevalence of AMD among multiethnic cohort

revealed that early AMD was present in 4.0% of the cohort and varied from 2.4% in

blacks to 6.0% in whites. However, common factors such as smoking, body mass

index, inflammatory factors, diabetes and alcohol were unable to explain the

significant difference in risk between whites and blacks (Klein et al., 2013).

1.1.3.4. Genetic factors

The genetic predisposition is a major risk factor for the development of AMD.

Evidence for a genetic contribution is undebatable and comes from classical

epidemiological and twin studies and gene-mapping studies (Heiba et al., 1994;

Klein et al., 1994; Klaver et al., 1998). In the largest twin study published to date, it

has been reported that 71% of AMD risk can be attributed to genetic influences

(Seddon et al., 2005).

1.1.3.4.1. The 1q31 locus

In 2005, a pioneering genome-wide association study (GWAS) suggested

complement factor H (CFH) gene on 1q31 as the first major AMD susceptibility gene

(Fisher et al., 2005). Simultaneously, several studies have validated this result

(Hageman et al., 2005; Haines et al., 2005; Klein et al., 2005; Zareparsi et al., 2005).

Among more than 20 variants in and around the CFH gene, rs1061170 (c.1204C>T)

accounts for an amino acid substitution (Y402H) in the CFH protein (Klein et al.,

2005). This synonymous single nucleotide polymorphism (SNP) lies within a

haplotype conferring an increased risk for developing AMD by a factor of 2.5 in

heterozygous carriers and by a factor of 6.2 in homozygous carriers (Conley et al.,

2006). Another common polymorphism in CFH, rs1410996 (c.2237-543G>A), was

also discovered which, along with rs1061170, has been found to be liable for 17% of

AMD susceptibility (Li et al., 2006; Maller et al., 2006; Raychaudhuri et al., 2011).

Another rare non-synonymous variant, CFH (c.3628C>T), which accounts for an

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amino acid substitution (R1210C), increases AMD risk by >20-fold and is virtually

absent in the control individuals (1 heterozygous carrier in 2,268 sequenced

controls) (Zhan et al., 2013). In addition to simple sequence changes, deletions in

two genes near CFH, CFHR1 and CFHR3, have been associated with reduced risk

of AMD (Hughes et al., 2006; Spencer et al., 2008).

1.1.3.4.2. The 10q26 locus

The second major AMD susceptibility locus is located on chromosome 10q26. The

linkage to the genetic region at chromosomal segment 10q26 was first identified by

(Weeks et al., 2000). In subsequent fine-mapping efforts designed to narrow the

chromosome 10q26 linkage peak, the association signals were refined to three

genes, namely, pleckstrin homology domain containing family A member 1

(PLEKHA1), age-related maculopathy susceptibility 2 (ARMS2, also known as

LOC387715) and high temperature requirement factor A1 (HTRA1) (Jakobsdottir et

al., 2005). PLEKHA1 was later excluded as an AMD susceptibility gene (Rivera et

al., 2005; Schmidt et al., 2006). As per the haplotype analysis done by Fritsche et al.

(2008), the AMD-associated polymorphisms, that are fine-mapped on 10q26,

continue on a 23.3 kb gene region. Overall, 15 AMD-associated polymorphisms were

identified in this region (Figure 4), which are in a strong linkage disequilibrium (LD)

to each other (Fritsche et al., 2008).

Figure 4: Schematic overview of the AMD-associated 23.3 kb region on chromosome 10q26 exhibiting high linkage equilibrium. Stars indicate the 15 AMD risk-associated polymorphisms; dark blue: ARMS2 exons 1 and 2; light blue: ARMS2 intron; white: intergenic region; dark red: HTRA1 exon 1; light red: HTRA1 intron 1. Adapted from Friedrich et al. (2011).

Further studies confirmed that all these polymorphisms occur together as an AMD-

associated risk haplotype (Yang et al., 2006; Wang et al., 2009; Wang et al., 2010b).

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Eleven of these polymorphisms are intronic or intergenic, and two are synonymous

SNPs in HTRA1 exon 1. The remaining two polymorphisms are located in the two

exons of ARMS2. A non-synonymous SNP rs10490924 (c.205G>T) in the exon 1 of

ARMS2 leads to an amino acid substitution in the ARMS2 protein at position 69

(A69S), while EU427539 refers to a combination of a deletion and an insertion

polymorphism within the 3' untranslated region (UTR) of the ARMS2 gene

(*372_815delins54) (Fritsche et al., 2008).

There are a number of contradictory reports about the effect of these polymorphisms

on the functions of ARMS2 or HTRA1. The amino acid substitution caused by

rs10490924 could bring a functional change in the ARMS2 protein (Wang et al.,

2009). The polymorphism EU427539, was shown to cause a destabilizing effect on

the ARMS2 mRNA (Fritsche et al., 2008; Friedrich et al., 2011). However, other

studies suggest that no effect of EU427539 polymorphism on the transcription of

ARMS2 gene could be detected (Kanda et al., 2010; Wang et al., 2010b). Moreover,

it has been shown that a common non-risk-associated non-synonymous variant,

rs2736911 (c.112C>T), also leads to decreased ARMS2 transcript levels (Friedrich

et al., 2011). Many contradictory reports were also shown either to have or not to

have an influence of AMD-associated polymorphisms on expression of HTRA1.

Some evidences show a two- to three-fold upregulation in HTRA1 expression due to

the risk-associated polymorphisms (Yang et al., 2006; Chan et al., 2007; An et al.,

2010; Yang et al., 2010; Iejima et al., 2015). These results are consistent with a

transgenic mouse model that ubiquitously overexpresses HTRA1 and exhibits

characteristics similar to those of wet AMD patients (Jones et al., 2011). However, in

other publications, no effect of AMD haplotypes was found on the HTRA1 expression

level (Kanda et al., 2007; Chowers et al., 2008; Kanda et al., 2010; Wang et al.,

2010a; Friedrich et al., 2011; Wang et al., 2013). The effect of two AMD risk-

associated synonymous polymorphisms in exon 1 of the HTRA1, namely rs1049331

(c.102C>T) and rs2293870 (c.108G>T), have been less studied because of the fact

that they do not account for any amino acid change. A recent study by Jacobo et al.

(2013) suggests that codon bias due to these synonymous polymorphisms results in

difference in translation speed of the HTRA1 protein, which might affect its structure

and function (Jacobo et al., 2013).

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1.1.3.4.3. Other AMD-associated gene loci

The recent meta-analysis of more than 17,100 AMD cases and more than 60,000

control subjects of European and Asian origin reveals 19 loci from as many as 19

chromosomes to be associated with AMD (Fritsche et al., 2013) (Figure 5). After

identification of strong association of CFH with AMD, subsequent association studies

showed additional complement genes to be strongly associated with AMD. These

include complement 2 (C2) and/or complement factor B (CFB) (Gold et al., 2006),

complement 3 (C3) (Yates et al., 2007) and complement factor I (CFI) (Fagerness et

al., 2009). In AMD lesions, presence of immunomodulators suggests that local

processes driven by complement dysregulation play a vital role in the development

and progression of AMD (Johnson et al., 2000; Anderson et al., 2010).

Figure 5: Nineteen common AMD risk variants in the discovery study of the AMD Gene Consortium meta-analysis. Summary of genome-wide association scan results in the discovery GWAS sample. Previously described loci before the meta-analysis reaching p < 5×10−8 are labeled in blue; newly found loci in the meta-analysis reaching p < 5×10−8 for the first time after follow-up are labeled in green. Adapted and modified from Fritsche et al. (2013).

Other than genes of complement system and genes on 10q26, functional analysis of

a few other genes highly associated with AMD explains the development of AMD

lesions. Hepatic lipase (LIPC) and cholesteryl ester transfer protein (CETP), found to

be associated with AMD, are expressed in the subretinal space and may participate

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in rapid cholesterol transfer from the RPE to the neural retina (Tserentsoodol et al.,

2006a; Tserentsoodol et al., 2006b). Apolipoprotein E (ApoE) has also been

detected in subretinal lesions where it promotes chronic inflammation in AMD (Levy

et al., 2015). Tissue inhibitor of metalloprotienase 3 (TIMP3) has been found to play

a pivotal role in ECM maintenance and remodeling in BrM (Fariss et al., 1997; Kamei

and Hollyfield, 1999). Apart from this, seven previously unknown loci discovered in

the meta-analysis are COL8A1-FILIP1L, IER3-DDR1, SLC16A8, TGFBR1, RAD51B,

ADAMTS9 and B3GALTL. Though weakly associated with the disease, functional

analysis of these genes might lead to a clear idea of AMD pathogenesis.

1.2. HTRA1 protein

The HTRA family of proteins is broadly characterized by a highly conserved protease

domain and one or more C-terminal protein-interaction domains (Singh et al., 2011;

Malet et al., 2012). The first described member of HTRA family, the Escherichia coli

(E.coli) HtrA protein (also called DegP or protease Do) is a periplasmic protein

(Clausen et al., 2002) that is upregulated under stress conditions such as heat shock

(Lipinska et al., 1988; Lipinska et al., 1989). HtrA functions as a chaperone and a

protease in a temperature-dependent fashion (Spiess et al., 1999).

1.2.1. Structure of HTRA1

Figure 6: Structure of HTRA1. Boxes represent protein domains; blue box indicates S, signal peptide domain cleaved in mature secreted protein; purple box indicates MAC25/IGFBP(insulin growth factor–binding protein) domain; yellow box indicates Kazal-like domain (KI); green box indicates serine protease domain, which contains Histidine (H), Aspargine (D) and Serine (S) forming a catalytic triad; red box indicates PDZ [postsynaptic density protein (psd95), drosophila disc large tumor suppressor (dlga), and zonula occludens-1 protein (zo-1) domain]. Adapted from An et al. (2010).

HTRA1, a secreted protease was first identified in 1996 as a gene expressed in

human fibroblast cells (Zumbrunn and Trueb, 1997). The degree of conservation

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between human HTRA1 and its bacterial homologues DegP is very high (Clausen et

al., 2002). Human HTRA1 is one of the four members of the human HTRA family

and consists of 480 amino acids (about 50 kDa), which are encoded by the nine

exons of the HTRA1 gene. Figure 6 depicts the structure of HTRA1. It has an N-

terminal signal peptide (S) for secretion. After secretion, a 22 amino acid-long signal

peptide of the HTRA1 protein is cleaved. The MAC25 domain has a homology to the

insulin like growth factor binding protein (IGFBP) and follistatin. Although the MAC25

domain showed homology initially to IGFBP, later it turned out that it has obvious

lower affinity for the insulin like growth factor (IGF) than other IGFBPs (Eigenbrot et

al., 2012). The homology to follistatin, however, led to the discovery that the MAC25

domain of HTRA1 protein can bind to activin, a member of transforming growth

factor-β (TGF-β) superfamily (Oka et al., 2004). Kazal inhibitors, specifically inhibit

serine proteases like trypsin and elastase (Rawlings et al., 2004). The largest

domain of HTRA1 is the protease domain with the catalytic triad of histidine (His220),

asparagine (Asp250) and serine (Ser328), which defines HTRA1 as serine protease

(Figure 6). By C-terminal protein interaction domain named “postsynaptic density

protein (psd95), drosophila disc large tumor suppressor (dlga), and zonula

occludens-1 protein (zo-1) domain” (PDZ), HTRA1 binds to its substrates.

Figure 7: Three-dimensional structure of HTRA1 trimer. Ribbon presentation of HTRA1 (inactive structure) in (A) top and (B) side views. The individual monomers are shown in different colors. Adapted and modified from Truebestein et al.(2011).

HTRA1 forms a trimeric structure which is mediated by N-terminal amino acid

residues of the protease domain (Figure 7) (Truebestein et al., 2011). The protein

interaction domain (PDZ) binds sequence-specifically to short C-terminal or internal

hydrophobic sequences of peptides. In this case, the PDZ domains of the members

of HTRA family are different, when it comes to ligand specificity. Previously, it was

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assumed that the PDZ domain of HTRA1 negatively regulates the proteolytic activity.

Thus, the protease activity is to be activated only when the PDZ domain binds to the

ligands (Murwantoko et al., 2004). However, based on the latest study of the crystal

structure of HTRA1 trimers, the research groups of Tim Clausen and Michael

Ehrmann showed that PDZ domain is not indespensible for the protease activity.

Instead substrate binding to the active site is sufficient to stimulate protease activity

(Truebestein et al., 2011). Accordingly, no allosteric ligands would be necessary for

the activation of HTRA1 as opposed to activation of bacterial HtrA.

1.2.2. Function of HTRA1

HTRA1 was reported to influence cell signaling (Oka et al., 2004; Zhang et al., 2012;

Graham et al., 2013; Supanji et al., 2013), organization of the ECM (Mauney et al.,

2010; Vierkotten et al., 2011), embryogenesis (De Luca et al., 2004) and skeletal

development and osteogenesis (Tsuchiya et al., 2005; Hadfield et al., 2008). Various

publications have identified an array of interaction partners of HTRA1 such as

members of TGF-β family (BMP3, GDF5, activin, TGF-βs) (Oka et al., 2004);

aggrecan, biglycan, decorin, fibromodulin, fibronectin, soluble type II collagen and

elastin (Tsuchiya et al., 2005); mature TGF-β1 (Launay et al., 2008); tubulin (Chien

et al., 2009); tuberous sclerosic complex 2 (Campioni et al., 2010); clusterin,

vitronectin, fibromodulin, alpha-2 macroglobulin and ADAM9 (An et al., 2010);

fibronectin, fibulin 5 and nidogen 1 (Vierkotten et al., 2011); pro-TGF-β1 (Shiga et

al., 2011); IGF-1(Jacobo et al., 2013); and TGF-β receptors II and III (Graham et al.,

2013). In addition, HTRA1 was suggested to influence the pathogenic processes of

several diseases including CARASIL (cerebral autosomal recessive arteriopathy with

subcortical infarcts and leukoencephalopathy) (Hara et al., 2009; Shiga et al., 2011),

osteoarthritic cartilage (Grau et al., 2006; Chamberland et al., 2009; Polur et al.,

2010), preeclampsia (Ajayi et al., 2008) and AMD (Vierkotten et al., 2011; Zhang et

al., 2012; Nakayama et al., 2014). HTRA1 may also exhibit tumor suppressor

activities (Baldi et al., 2002; De Luca et al., 2003; Chien et al., 2004), as

downregulation of the protein was observed in a variety of cancers, e.g.,

endometrial, ovarian or breast cancers, and melanomas (Baldi et al., 2002; Shridhar

et al., 2002; Chien et al., 2004; Bowden et al., 2006; Mullany et al., 2011; Wang et

al., 2012). Despite its suspected role in the various disease processes, the exact

mechanisms of HTRA1 are largely unknown.

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2. AIM OF THE STUDY

Several polymorphisms in HTRA1 located in chromosomes 10q26 have been

reported to be associated with AMD by various independent studies (Dewan et al.,

2006; Fritsche et al., 2013). Recently, two synonymous polymorphisms have been

found to have effect on the translational efficiency of HTRA1 and its binding capacity

of IGF-1 (Jacobo et al., 2013). The focus of our study was set on unraveling the

effect of AMD-associated synonymous SNPs rs1049331 (c.102C>T) and rs2293870

(c.108G>T) within exon 1 of HTRA1 on the conformation, secretion and substrate

affinity of HTRA1 protein. In several studies, HTRA1 has been shown to inhibit TGF-β

signaling (Oka et al., 2004; Launay et al., 2008; Shiga et al., 2011; Zhang et al.,

2012; Graham et al., 2013; Karring et al., 2013). So, the aim also included the

influence of synonymous polymorphisms on regulation of intracellular TGF-β

signaling. The final aim was to investigate the effect of HTRA1 on activation of

microglial cells.

MATERIALS AND METHODS

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3. MATERIALS AND METHODS

3.1. Materials

3.1.1. Chemicals and reagents

Table 2: Chemicals and reagents

Chemical/reagent Supplier

2-Nitrophenyl β-D-galactopyranoside (ONPG)

Sigma-Aldrich, St. Louis, MO, USA

2-Propanol Merck Chemicals GmbH, Schwalbach, Germany

4',6-diamidino-2-phenylindole (DAPI) Merck Chemicals GmbH, Schwalbach, Germany

5-Bromo-4-chloro-3-indolyl β-D-galactopyranoside (X-Gal)

AppliChem GmbH, Darmstadt, Germany

β-casein from bovine milk Sigma-Aldrich, St. Louis, MO, USA

β-Mercaptoethanol Sigma-Aldrich, St. Louis, MO, USA

Acetic Acid (100%) Merck Chemicals GmbH, Schwalbach, Germany

Albumin fraction V (BSA) AppliChem GmbH, Darmstadt, Germany

Ammonium persulfate (APS) AppliChem GmbH, Darmstadt, Germany

Agarose (Byozyme LE) Biozym Scientific GmbH, Hessisch Oldendorf, Germany

Ampicillin Carl Roth GmbH, Karlsruhe, Germany

Bacto™ Agar Becton, Dickinson and Company, Franklin Lakes, USA

Bacto™ Yeast Extract Becton, Dickinson and Company, Franklin Lakes, USA

Boric acid (H3BO3) Merck Chemicals GmbH, Schwalbach, Germany

Bromphenol blue Sigma-Aldrich, St. Louis, MO, USA

Casein from Bovine Milk Merck Chemicals GmbH, Schwalbach, Germany

Coomassie Billiant Blue (Thermo Scientific) VWR International, Germany


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Developer solution AGFA, Mortsel, Belgium

Dimethyl sulfoxide (DMSO) Merck Chemicals GmbH, Schwalbach, Germany

Ethanol J.T. Baker, Phillipsburg, USA

Ethanol ≥99,8 p.a Carl Roth GmbH, Karlsruhe, Deutschland

Ethidium bromide AppliChem GmbH, Darmstadt, Germany

Ethylenediaminetetraacetic acid (EDTA)

(Titriplex) Merck Chemicals GmbH, Schwalbach, Germany

FlAsH-EDT2 Invitrogen, Carlsbad, CA, USA

Fixer solution AGFA, Mortsel, Belgium

Glycerol AppliChem GmbH, Darmstadt, Germany

Hi-Di™ Formamide Applied Biosystems Inc., Foster City, USA

HTRA1(His-tagged) ProteaImmun GmbH, Berlin, Germany

Hydrogen chloride (HCl) Merck Chemicals GmbH, Schwalbach, Germany

Hydrogen peroxide Merck Chemicals GmbH, Schwalbach, Germany

Isopropyl β-D-1-thiogalactopyranoside (IPTG)


Luminol Sigma-Aldrich, St. Louis, MO, USA

Methanol Merck Chemicals GmbH, Schwalbach, Germany

Milk powder Carl Roth GmbH, Karlsruhe, Germany

p-Coumarin acid Sigma-Aldrich, St. Louis, MO, USA

Paraformaldehyde (PFA) Merck Chemicals GmbH, Schwalbach, Germany

Phenylmethanesulfonylfluoride (PMSF)

Merck Chemicals GmbH, Schwalbach, Germany

Roti® Quant Carl Roth GmbH, Karlsruhe, Germany

Rotiphorese Gel 40 (29:1) acrylamide/bisacrylamide

Carl Roth GmbH, Karlsruhe, Germany

Sodium acetate, trihydrate AppliChem GmbH, Darmstadt, Germany

Sodium carbonate (Na2CO3) Merck Chemicals GmbH, Schwalbach, Germany


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Sodium chloride (NaCl) VWR International, LLC, West Chester, USA

Sodium hydroxide (NaOH) Merck Chemicals GmbH, Schwalbach, Germany

Sodium dodecyl sulfate (SDS) Carl Roth GmbH, Karlsruhe, Germany

Sodium hydrogen carbonate (NaHCO3)


TCEP HCl Thermo Fisher Scientific, Hudson, USA

Tetramethylethylenediamine (TEMED)


Tris(hydroxymethyl)aminomethane (Tris)

Merck Chemicals GmbH, Schwalbach, Germany

Trypsin EDTA in 10X Buffer PAA Laboratories GmbH, Parsching, Austria

TPCK-trypsin Sigma-Aldrich, St. Louis, MO, USA

Tryptone/Peptone from casein Carl Roth GmbH, Karlsruhe, Germany

Xylene cyanol Sigma-Aldrich, St. Louis, MO, USA

X-gal (5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside)

AppliChem GmbH,Darmstadt, Germany

Recombinant TGF-β1 PeproTech, Hamburg, Germany

Recombinant interleukin 4 (IL4) PeproTech, Hamburg, Germany

Lipopolysaccharide (LPS) from E.coli Sigma-Aldrich, St. Louis, MO, USA

3.1.2. Kits and ready-made solutions

Table 3: Kits and ready-made solutions

Kit/solution Supplier Application

10X ThermoPol Reaction Buffer

New England BioLabs® Inc., Ipswich, USA

A-tailing of PCR products

5X GoTaq® Reaction Buffer Kit

Promega Corporation, Madison, USA

PCR

Griess Reagent System Promega Corporation, Madison, USA

Nitrite measurement

AccuPrime™ Taq Polymerase PCR

Life Technologies, Carlsbad, CA, USA

PCR


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BigDye® Terminator v1.1 Cycle Sequencing Kit

Applied Biosystems Inc., Foster City, USA

Cycle sequencing

dNTP-Set Genaxxon BioScience GmbH, Ulm, Germany

PCR, cDNA synthesis

Luciferase Assay Reagent


Luciferase assay

NEBuffer CutSmart® New England BioLabs® Inc., Ipswich, USA

Restriction digestion

NucleoBond® Xtra Midi MACHEREY-NAGEL GmbH & Co. KG, Düren, Germany

Plasmid isolation

NucleoSpin® Extract II MACHEREY-NAGEL GmbH & Co. KG, Düren, Germany

Gel extraction of DNA fragments

NucleoSpin® Plasmid MACHEREY-NAGEL GmbH & Co. KG, Düren, Germany

Plasmid isolation

GeneRuler™ DNA Ladder Mix

Fermentas International Inc., Burlington, Canada

Marker for DNA electrophoresis

PageRuler™ Prestained Protein Ladder


Marker for protein electrophoresis

pGEM®-T Vector System


Subcloning

Reporter Lysis 5X Buffer Promega Corporation, Madison, USA

Luciferase assay

RevertAid™ 5X Reaction Buffer


cDNA synthesis

RNAlater® Applied Biosystems/Ambion, Austin, USA

RNA purification

RNase-Free DNase Set Qiagen N.V., Hilden, Germany RNA purification

RNeasy® Mini Kit Qiagen N.V., Hilden, Germany RNA purification

SuperSignal West Femto Maximum Sensitivity Substrate

Thermo Fisher Scientific, Hudson, USA

Immunoblot: protein detection by chemiluminescence

T4 DNA Ligase Reaction Buffer (10X)


Ligation

Taq buffer 15 mM MgCl2 (10X)


PCR

TaqMan® Gene Expression Master Mix


qRT-PCR


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TC-FlAsH™ II In-Cell Tetracysteine Tag Detection (Green Fluorescence)

Invitrogen, Carlsbad, CA, USA MicroScale Thermophoresis

TransIT®-LT1 Transfection Reagent

Mirus Bio LLC, Madison, USA Transfection

SuperSignal West Femto Maximum Sensitivity Substrate

VWR International Germany GmbH, Darmstadt, Germany

Western blot

Twin-Strep® Kit IBA GmbH, Göttingen, Germany Recombinant protein purification

3.1.3. Buffers and solutions

Table 4: Buffers and solutions

Buffer Chemical Composition Percentage Composition

Blocking solution Milk powder 1X PBS or 1XPBST

3% (w/v)

Coomassie stainer Methanol Acetic acid Coomassie Brilliant Blue

30% 10% 0.10% w/v

Coomassie destainer

Methanol Acetic acid

30% 10%

DNA-loading buffer (10X)

Tris-HCl pH 7.5, Sodium acetate, EDTA, Glycine, Bromphenol blue Xylene cyanol

10 mM 5 mM 2 mM 10% v/v 0.001% v/v 0.001% v/v

ECL solution 1 Luminol 0.1 M Tris-HCl

50 mg 200 ml

ECL solution 2 p-Coumarin acid DMSO

0.0011%

Laemmli buffer Tris-HCl pH 6.8, Glycerine β-mercaptoethanol SDS Bromphenol blue

195 mM 30% 10% 6% 0.75%


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Buffer Chemical Composition Percentage Composition

PBS (10X) NaCl Na2HPO4 KCl KH2PO4, pH 7.4

1.37 M 100 mM 27 mM 18 mM

SDS-running buffer (10X)

Tris-HCl Glycine SDS, pH 8.6

25 M 2 M 10% (w/v)

Digestion buffer pH 7.5

Tris-HCl NaCl CaCl2

50 mM 150 mM 5 mM

Casein solution Casein from bovine milk Digestion buffer pH 7.5

0.02%

TBE (5X) Tris-HCl, H3BO3, EDTA, pH 7.4

0.5 M 0.5 M 10 mM

TE buffer Tris-HCl EDTA, pH 7.5 or pH 8.0

10 mM 1 mM

TRIS buffer saline Tris-HCl [pH 8.0], NaCl

150 mM 100 mM

Towbin transfer buffer

Tris-HCl Glycine Methanol

0.025 M 0.192 M 20%

Twin-Strep® Elution Buffer (Buffer E)

Tris-HCl pH 8 NaCl EDTA Desthiobiotin

100 mM 150 mM 1 mM 2.5 mM

Twin-Strep® Regeneration Buffer (Buffer R)

Tris-HCl pH 8 NaCl EDTA HABA

100 mM 150 mM 1 mM 1 mM

Twin-Strep® Wash Buffer (Buffer W)

NaCl EDTA Tris-HCl pH 8

150 mM 1 mM 100 mM

X-Gal solution X-Gal DMSO

0.04% w/v

Xylene cyanol Xylene cyanol Glycerin (87%) H2O dest.

0.1% v/v 40% v/v Ad. 50 ml


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3.1.4. Cell lines

3.1.4.1. Bacterial cells

Table 5: E.coli strains

Strain Genotype Reference/Origin

E.coli

strain

DH5α

fhuA2 Δ(argF-lacZ)U169 phoA glnV44 Φ80

Δ(lacZ)M15 gyrA96 recA1 relA1 endA1 thi-1

hsdR17

Life Technologies,

Carlsbad, CA, USA

E.coli

strain

JM110

rpsL (Strr) thr leu thi-1 lacY galK galT ara tonA

tsx dam dcm supE44 ∆(lac-proAB) [F´ traD36

proAB lacIq Z∆M15].

Life Technologies,

Carlsbad, CA, USA

3.1.4.2 Mammalian cell lines

Table 6: Mammalian cell lines

Cell line Origin Culture

Hek293-Ebna cells

Human embryonic kidney cells

Invitrogen, Carlsbad, CA, USA

MLEC-PAI/Luc cells

Mouse lung epithelial cells (MLEC) stably transfected with an expression construct containing a truncated PAI-1 promoter fused to the firefly luciferase reporter gene

ATCC, Manassas, VA, USA

BV-2 Mouse microglia cells Kindly provided by Prof. Thomas Langmann, Department of Ophthalmology, University of Cologne, Germany


27 | P a g e

3.1.4.3. Media and supplements

Table 7: Cell culture media and supplements

Cell culture medium Suppliers

DMEM High Glucose (4.5 g/l) with L-Glutamine

(Gibco) Life Technologies GmbH, Darmstadt, Germany

Fetal Bovine Serum Gold (FBS) (Gibco) Life Technologies GmbH, Darmstadt, Germany

Opti-MEM® I Reduced Serum Media (Gibco) Life Technologies GmbH,Darmstadt, Germany

Genticin Sulphate (G418) Solution (PAA) GE Healthcare, Galfont St Giles, United Kingdom

Penicillin/Streptomycin/L-Glutamine (PAA) GE Healthcare, Galfont St Giles, United Kingdom

RPMI 1640 without L-Glutamine (PAA) GE Healthcare, Galfont St Giles, United Kingdom

Dulbecco’s 10X PBS (Gibco) Life Technologies GmbH,Darmstadt, Germany

Table 8: Medium for cultivation of E.coli

Medium Composition

LB medium 1% NaCl, 1% Tryptone/Peptone from casein, 0.5% Bacto™ Yeast Extract

LB medium for cultivation of E.coli was sterilized by autoclaving and stored at 4°C.

For casting of plates, 1.5% Bacto™ Agar were added to the medium prior to

autoclaving. If needed, autoclaved medium was supplemented with 100 µg/ml

ampicillin (LBAmp).

3.1.5. Enzymes

Table 9: Enzymes

Enzyme Supplier Application

AccuPrime™ Taq Polymerase

Life Technologies, Carlsbad, CA, USA PCR

XhoI New England BioLabs® Inc., Ipswich, USA



28 | P a g e

Enzyme Supplier Application

BseRI New England BioLabs® Inc., Ipswich, USA


FseI New England BioLabs® Inc., Ipswich, USA


NotI-HF™ New England BioLabs® Inc., Ipswich, USA


DpnI New England BioLabs® Inc., Ipswich, USA

Methylated sequence cutter

PfuUltra HF DNA polymerase

Agilent Technologies, CA, USA PCR

Proteinase K Merck KGaA, Darmstadt, Germany DNA isolation

RevertAid™ M-MuLV Reverse Transcriptase


cDNA synthesis

T4 DNA Ligase New England BioLabs® Inc., Ipswich, USA

Ligation

Taq DNA Polymerase


A-tailing of PCR products

3.1.6. Antibodies

Table 10: Primary antibodies (mAB: monoclonal antibody; pAB: polyclonal

antibody; WB: Western blot)

Antibody Type Species Dilution Use Reference

HTRA-1 (AB 65902)

pAB Rabbit 1:2000 WB abcam, Cambridge, United Kingdom

β-actin(ACTB)

mAB Mouse 1:10,000 WB Sigma-Aldrich, St. Louis, MO, USA

TGF-β1(SC-146)

pAB Rabbit 1:10,000 WB Santa CruzBiotechnology, Inc., Dallas, TX, USA

SMAD-2 pAB Rabbit 1:1000 WB Cell Signaling Technology, Beverly, MA, USA

pSMAD-2 pAB Rabbit 1:1000 WB Cell Signaling Technology, Beverly, MA, USA


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Table 11: Secondary antibodies

Antibody Supplier Application

Goat anti-mouse IgG, peroxidase conjugate

(Calbiochem) Merck Chemicals GmbH, Schwalbach, Germany

WB

Goat anti-rabbit IgG, peroxidase conjugate

(Calbiochem) Merck Chemicals GmbH, Schwalbach, Germany

WB

3.1.7. Vectors and plasmids

Table 12: Starting vectors

Vector Supplier

pCEP4 Invitrogen™, Carlsbad, USA

pGEM®-T Vector Promega Corporation, Madison, USA

pEXPR-IBA103 IBA GMBH, Göttingen, Germany

Table 13: Control plasmids and plasmids with insert already available

Plasmid Supplier

“pGEM®-T + HTRA1 mRNA non-risk” (pGEM®-T construct for HTRA1:CG variant)

Institute of Human Genetics, University Hospital Regensburg, Regensburg, Germany

“pCEP4 + HTRA1 mRNA non-risk” (pCEP4 construct for HTRA1:CG variant)


“pCEP4 + HTRA1 mRNA risk” (pCEP4 construct for HTRA1:TT variant)


“pEXPR-IBA103 + HTRA1 mRNA non-risk” (pEXPR-IBA103 construct for HTRA1:CG variant)


“pEXPR-IBA103 + HTRA1 mRNA risk” (pEXPR-IBA103 construct for HTRA1:CG variant)



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3.1.8. Oligonucleotides

The following oligonucleotides were used as primers and were supplied by Metabion

International AG (Martinsried, Germany).

Table 14: Sequence and use of oligonucleotides for PCR in the study

Primer Name Sequence 5’-3’ Use

Htra1- endoBseRI-F

CAAAATTGACCACCAGGGCAAGC

Cloning TC-tagged HTRA1:CG or HTRA1:TT in pCEP4 vector

Htra1-TCtag-R GCAACAGCCAGGACAACATGGGTCAATTTCTTCGGGAATCACTGTGAT

Cloning TC-tagged HTRA1:CG in pCEP4 vector

Htra1-TCtag-R2 AAGAAATTGACCCATGTTGTCCTGGCTGTTGCTAGCTCGAG


Htra1-TCtag-R2 (rev. comp.)

CTCGAGCTAGCAACAGCCAGGACAACATGGGTCAATTTCTT


Htra1-mRNA-NotI-F

GCGGCCGCGCGCACTCGCACCCGCT

Cloning TC-tagged HTRA1:TT in pCEP4 vector

Htra1-RT-R2 GATGGCGACCACGAACTC Cloning TC-tagged HTRA1:TT in pCEP4 vector

HTRA1_SN_MUT_F

GCAGCGGTCTGGGCACCCGGCGGCCAAAGGC

Cloning TC-tagged HTRA1:CC in pCEP4 vector

HTRA1_SN_MUT_R

GCCTTTGGCCGCCGGGTGCCCAGACCGCTGC

Cloning TC-tagged HTRA1:CC in pCEP4 vector

HTRA1-RT-F2 AGCAGACATCGCACTCATCA Sequencing of HTRA1 constructs

HTRA-Ex1-F0 AGGCCCTCCTGCACTCT Sequencing of HTRA1 constructs

HTRA1-Ex1-R2 CGCCGCACGGGCCCTCC Sequencing of HTRA1 constructs

HTRA1-Ex2-R GCCATCTTCCCACCACGT Sequencing of HTRA1 constructs


31 | P a g e

Primer Name Sequence 5’-3’ Use

HTRA1-Ex1-F AGAGCGCCATGCAGATCC Sequencing of HTRA1 constructs

pEXPR-IBA103-F GAGAACCCACTGCTTACTGGC Sequencing of Strep-tagged constructs

pEXPR-IBA103-R TAGAAGGCACAGTCGAGG Sequencing of Strep-tagged constructs

M13-F CACGACGTTGTAAAACGAC Sequencing of pGEM®-T constructs

M13-R GGATAACAATTTCACACAGG Sequencing of pGEM®-T constructs

Table 15: Primers for first strand cDNA synthesis

Name Supplier

Random hexamer primer Fermentas International Inc., Burlington, Canada

Table 16: Probes and oligonucleotides used for qRT-PCR

Gene Primer Name Sequence Roche Probe #

Arg1 mARG1_RT_F1 GAATCTGCATGGGCAACC 2

mARG1_RT_R1 GAATCCTGGTACATCTGGGAAC

Ym1 mYM1-RT-F3 AAGACACTGAGCTAAAAACTCTCC 88

mYM1-RT-R3 GAGACCATGGCACTGAACG

IL6 mIL6-RT-F CCAGGTAGCTATGGTACTCCAGA 6

mIL6-RT-R GATGGATGCTACCAAACTGGAT

iNOS mINOS-RT-F CTTTGCCACGGACGAGAC 3

mINOS-RT-R TCATTGTACTCTGAGGGCTGA

Mouse ATPase

mATPase-RT-F CAGCAGATTTTAGCAGGTGAA 77

mATPase-RT-R CTGCCAGCTTATCAGCCTTT


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3.1.9. Consumables

Table 17: Consumables

Consumable Supplier

96-well Plate Applied Biosystems Inc., Foster City, USA

Amicon Ultra 10,000 Kw 4 ml columns

Merck, Millipore, Billercia, MA, USA

Cell culture dishes: 100 mm x 20 mm style

Sarstedt AG & Co., Nümbrecht, Germany

Cell culture plates: 12-well Corning Incorporated, Corning, USA

Cell culture plates: Costar 6-well

Corning Incorporated, Corning, USA

Cell scraper 30 cm Orange Scientific, Braine-l'Alleud, Belgium

Disposable gloves: Blue VWR International Germany GmbH, Darmstadt, Germany

Disposable gloves: Green Kimberly-Clark Professional, Roswell, GA, USA

Disposable scalpels Feather Safety Razor Co., Ltd., Osaka, Japan

Falcon tubes (15, 50 ml) Sarstedt AG, Nürnbrecht, Germany

Forceps: Dumont #5 Fine Science Tools GmbH, Heidelberg, Germany

Insulin syringes: BD Micro-Fine™+ 0.5 ml 0.33 mm (29G) x 12.7 mm U-100

Becton, Dickinson and Company, Franklin Lakes, USA

Magnetic stir bars Carl Roth GmbH + Co. KG, Karlsruhe, Germany

Micro tubes: SafeSeal Micro Tubes

Sarstedt AG, Nürnbrecht, Germany

Microplate: LUMITRAC™ 200 Greiner Bio-One GmbH, Frickenhausen, Germany

PCR stripes: PCR SoftStrips Biozym Scientific GmbH, Oldendorf, Germany

Pipette filter tips: SafeSeal-Tips Professional

Biozym Scientific GmbH, Oldendorf, Germany

Pipette tips (10,100, 1000 µl) VWR International Germany GmbH, Darmstadt, Germany

Pipette tips (qRT-PCR): 30 µl (384 tips)


Plastic weighing trays VWR International Germany GmbH, Germany

PVDF transfer membrane: Immobilon-P

Millipore Corporation, Billerica, USA


33 | P a g e

Consumable Supplier

qRT-PCR plates: MicroAmp Optical 384-Well Reaction Plate


Scissors: HB 7459 HEBUmedical GmbH, Tuttlingen, Germany

Scissors: Vannas-Tübingen Spring Scissors - 5mm Blades

Fine Science Tools GmbH, Heidelberg, Germany

Standard Capillaries NanoTemper Technologies, Munich, Germany

Standard Cuvettes Sarstedt AG, Nürnbrecht, Germany

Syringe needles: Microlance 3 20G


Syringes: Plastipak 1 ml Becton, Dickinson and Company, Franklin Lakes, USA

Tissue culture flasks: 175 cm2 (vented cap)


Tissue culture flasks: 25 cm2 (filter cap)

TPP AG, Trasadingen, Switzerland

Tissue culture flasks: 75 cm2 (PE vented cap)

Sarstedt AG, Nürnbrecht, Germany

Whatman Paper: 3M Chr Whatman plc, Maidstone, UK

X-ray film: CRONEX™ 5 (13x18)

Agfa-Gevaert N.V., Mortsel, Belgium

3.1.10. Instruments

Table 18: Instruments

Instrument Name Supplier

Automated sample disruption

TissueLyser Qiagen N.V., Hilden, Germany

Balance SBC52 Scaltec Instruments GmbH, Göttingen, Germany

Capillary sequencer 3130xl Genetic Analyzer


Centrifuge (for cell culture)

5810 Eppendorf AG, Hamburg, Germany

Tabletop centrifuge Biofuge fresco Heraeus Holding GmbH, Hanau, Germany


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Cold Falcon centrifuge

Megafuge 1.0R Heraeus Holding GmbH, Hanau, Germany

Falcon centrifuge Megafuge 3L Heraeus Holding GmbH, Hanau, Germany

Clean bench (for bacteria)

Hera guard HPA 12/65


Dark Hood DH-40 biostep GmbH, Jahnsdorf, Germany

Dark Hood (printer) P93D Mitsubishi Electric Corporation, Tokyo, Japan

DNA electrophoresis system

BlueMarine 200 Serva Electrophoresis GmbH, Heidelberg, Germany

Flake-ice machine AF100 Scotsman Ice Systems, Vernon Hills, USA

Fluorescence microscope

Axioskop 2 mot plus

Carl Zeiss GmbH, Jena, Germany

Fluorescence microscope (camera)

AxioCam MRc Carl Zeiss GmbH, Jena, Germany

Fluorescence microscope (light-source)

FluoArc Carl Zeiss GmbH, Jena, Germany

Image scanner Fujifilm FLA-5000

Fujifilm, Düsseldorf, Germany

Incubation hood Certomat® HK B. Braun Biotech International GmbH, Melsungen, Germany

Incubator (for bacteria)

INB 400 Memmert GmbH + Co. KG, Schwabach, Germany

Incubator (for cell culture)

CB 210 Binder GmbH, Tuttlingen, Germany

Laminar flow (for cell culture)

SK1200 BDK Luft- und Reinraumtechnik GmbH, Sonnenbühl-Genkingen, Germany

Microscope (for cell culture)

DM IL Leica Microsystems GmbH, Wetzlar, Germany

MicroScale Thermophoresis

Monolith NT.115 NanoTemper Technologies, Munich, Germany

Microwave Kor-6D07 Daewoo Electronics Europe GmbH, Butzbach, Germany

MilliQ water system MilliQ synthesis Millipore Corporation, Billerica, USA


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Minicentrifuge Spectrafuge Mini Labnet International Inc., Edison, USA

Multichannel pipette (10µl)

Discovery Eight-Channel Pipette

Labnet International Inc., Edison, USA


Research (multi-channel)

Eppendorf AG, Hamburg, Germany


Matrix Equalizer Pipette


Pipette controller Accu-jet Brand GmbH + Co. KG, Wertheim, Germany

Pipettes (10, 100 and 1000 µl)

Research Eppendorf AG, Hamburg, Germany

Plate reader FLUOstar OPTIMA

BMG LABTECH GmbH, Offenburg, Germany

Plate reader Magellan™ Plate Reader

TECAN US, Durham, NC, USA) (available at Institute of Clinical Chemistry, University Hospital Regensburg, Regensburg, Germany)

Power supply (DNA electrophoresis)

Blue PowerPlus Serva Electrophoresis GmbH, Heidelberg, Germany

Power supply (protein electrophoresis)

Blue Power 500 Serva Electrophoresis GmbH, Heidelberg, Germany

Protein electrophoresis system

Mini Protean 3 System

Bio-Rad Laboratories Inc., Hercules, USA

Semi-dry electrophoretic transfer cell

Trans-Blot SD Bio-Rad Laboratories Inc., Hercules, USA

Shaker Rocking Platform VWR International, LLC, West Chester, USA

Shaker (for bacteria) Certomat® R B. Braun Biotech International GmbH, Melsungen, Germany

Spectrophotometer Amersham Biosciences Ultrospec 2100

GE Healthcare Bio-Sciences AB, Uppsala, Sweden

Spectrophotometer Nanodrop ND-1000



36 | P a g e


Thermocycler T1/T300 Thermocycler

Biometra biomedizinische Analytik GmbH, Göttingen, Germany

Thermocycler qRT-PCR

ViiA™ 7 System Taqman

Life Technologies, Carlsbad, CA, USA

Thermomixer Thermomixer compact

Eppendorf AG, Hamburg, Germany

Transilluminator UST-30_M-8R BioView Ltd., Billerica, MA, USA

Ultrasonic processor Vibra Cell™

VCX400 Sonics & Materials, Inc., Newtown, USA

Vortexer Vortexer Genie 2 Scientific Industries, Bohemia, USA

Water bath Medingen WBT 12

P-D Industriegesellschaft mbH Prüfgerätewerk Dresden, Dresden, Germany

Water still (for buffers)

2012 GFL Gesellschaft für Labortechnik GmbH, Burgwedel, Germany

3.1.11. Software tools

Table 19: Software tools

Software Application Supplier

Ape Plasmid Editor

Sequence Analysis M.Wayne Davis, Department of Biology, University of Utah, USA

ArgusX1 V4.0.81 Agarose gel documentation

biostep GmbH, Jahnsdorf, Germany

BioEdit Sequence Alignment Editor v7.0.9.0

Sequence alignment Tom Hall, Ibis Therapeutics, Carlsbad, USA

KaleidaGraph 4.1 MicroScale Thermophoresis

NanoTemper Technologies, Munich, Germany

Magellan™ Plate

Reader

NO assay TECAN US, Durham, NC, USA

(available at the Institute of Clinical

Chemistry, University Hospital,

Regensburg, Germany)


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Software Application Supplier

Nanodrop ND-1000 v.3.5.2

Measurement of DNA/RNA concentration


ViiA™ 7 System software

qRT-PCR data analysis Applied Biosystems Inc., Foster City, USA

Total lab TL100 software

Densitometry of immunoblots

Nonlinear Dynamics, Durham, NC, USA

3.2. Methods

3.2.1. Cultivation of mammalian cell lines

Cells were grown in a humidified incubator at 37°C (atmosphere 95% air, 5% CO2).

The medium, cultivation volume and growth supply are described in Table 20.

Table 20: Cultivation of mammalian cells

Cell line Growth medium Growth supply

Cultivation volume

Hek293-Ebna

DMEM High Glucose (4,5 g/l) with L-Glutamine

100 mm dish

10 ml

BV-2 RPMI 1640 without L-Glutamine 75 cm2 flask

10 ml

MLEC-PAI/Luc

DMEM High Glucose (4,5 g/l) with L-Glutamine

100 mm dish

10 ml

DMEM High Glucose (4.5 g/l) with L-Glutamine was supplemented with 10% FBS, 100

U/ml penicillin and 0.1 mg/ml streptomycin. RPMI 1640 without L-Glutamine is

supplemented with 5% FBS, 1% of L-Glutamine, 195 nM β -Mercaptoethanol, 100

U/ml penicillin and 0.1 mg/ml streptomycin. For Hek293-Ebna cells, 1% G418 was

also supplemented with the DMEM High Glucose medium.

Human Embryonic Kidney 293 (Hek293)-Ebna cells and MLEC-PAI/Luc (Mink lung

epithelial cells stably transfected with an expression construct containing a truncated

PAI-1 promoter fused to the firefly luciferase reporter gene) cells were split for

propagation at an estimated confluence of 90%. To this end, the medium was

aspirated and both the cells were washed once with 5 ml of PBS.


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For Hek293-Ebna cells, no trypsinization was required for the detachment of cells.

After washing with 1X PBS, corresponding media was pipetted gently onto the cells.

Cells with media were collected in sterile 15 ml Falcon tubes. The cells were pelleted

by centrifuging at a speed of 1000 rpm in centrifuge for Falcon tubes in cell culture

(5810 Eppendorf Centrifuge) at RT. After equally redistributing the cells in the total

amount of solution, they were split 1:6 to a final volume of 10 ml on a new cell culture

dish.

On the other hand, 2 ml of trypsin-EDTA solution was pipetted directly onto the

MLEC-PAI/Luc cells. The dishes of MLEC-PAI/Luc cells were then incubated for 3-5

min at 37°C, allowing the cells to detach from the plastic surface. As soon as most

cells were floating, the trypsinization process was stopped by adding 10 ml of the

corresponding medium. Cells with media were collected in sterile 15 ml Falcon tubes.

The cells were pelleted by centrifuging at a speed of 1000 rpm in centrifuge for

Falcon tubes (in cell culture) at RT. After equally redistributing the cells in the total

amount of solution, they were split 1:6 to a final volume of 10 ml on a new cell culture

dish.

BV-2 microglial cells were split according to a different procedure: first the medium

was aspirated and replaced with 10 ml of fresh medium. Cells were then scraped and

3 ml of the suspension were transferred to a fresh flask containing 10 ml of medium.

3.2.2. Cultivation of E.coli

E.coli cells were cultivated either on LBAmp dishes or in liquid LBAmp medium (for the

medium’s composition, see Table 8). Plates were incubated ON at 37°C; liquid

cultures were shaken ON at 37°C to ensure oxygen circulation.

3.2.3. Cloning strategy

3.2.3.1. Amplification of DNA fragments

Three variants of the HTRA1 gene [the most frequent HTRA1 haplotype

(HTRA1:CG), as well as two less common HTRA1 haplotypes (HTRA1:TT and

HTRA1:CC)] were tagged with a six-amino acid-long Tetracysteine (TC) tag at the C-

terminal end and were cloned into the pCEP4 vector (Invitrogen, Carlsbad, CA, USA).


39 | P a g e

The expression constructs for HTRA1:CG, HTRA1:CC and HTRA1:TT variants

tagged with TC-tag were constructed by the following procedure.

To obtain the TC-tagged HTRA1:CG and HTRA1:TT expression constructs, two

steps of amplification were performed. In the first step, coding sequence of

HTRA1:CG and HTRA1:TT without the stop codon were amplified from expression

construct for untagged HTRA1:CG variant (an already existing construct available at

the Institute of Human Genetics, University Hospital Regensburg, Regensburg,

Germany) and from cDNA of ARPE-19 (human retinal pigment epithelium) cells

respectively. It is to be noted that ARPE-19 cells are heterozygous for the

synonymous polymorphisms rs1049331 and rs2293870 (TT/CG). Thus amplified

coding sequences were subcloned into the pGEM®-T vector for sequencing (see the

procedure below). In the second step, after sequencing, the correct cDNAs were

amplified with two reverse complimentary oligonucleotides to introduce a six-amino

acid-long (Cysteine-Cysteine-Proline-Glycine-Cysteine-Cysteine), peptide sequence

at the C-terminal end, the “TC-tag. Thus obtained TC-tagged coding sequence of

HTRA1:CG and HTRA1:TT in pGEM®-T was then restriction digested and ligated into

the pCEP4 vector (see the procedure below).

The PCR reaction mixture for amplification of HTRA1:CG and HTRA1:TT fragments

was prepared according to the following protocol:

AccuPrime™ PCR Buffer B (5X) 2.5 μl

5'-Primer (5 µM) 1 μl


AccuPrime™ Taq Polymerase 0.25 μl

Template (cDNA/plasmid) 100-200 ng

H2O upto 25 μl

PCRs were performed with the following cycling conditions:

Initial denaturation 95°C 5 min

Denaturation 95°C 30 s

Annealing 59°C 30 s 30x

Extension 72°C 45 s

Final extension 72°C 5 min


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The expression construct for TC-tagged HTRA1:CC was obtained by first introducing

site-directed mutagenesis in pGEM®-T construct for HTRA1:CG variant (available at

the Institute of Human Genetics, University Hospital Regensburg, Regensburg,

Germany). The correct sequences were detected by sequencing. Then the amplified

product was digested by DpnI to get rid of methylated parent plasmid. The amplified

product was then restriction digested from pGEM®-T vector and ligated into the

pCEP4 vector (see the procedure below). The PCR reaction mixture for amplification

of HTRA1:CC fragments was prepared according to the following protocol:

Pfu Ultra HF buffer(10X) 5 μl

dNTPs (2.5 mM each) 2 μl



DMSO 2 μl

PfuUltra HF DNA polymerase 1 μl

Template (plasmid DNA) 100-200 ng

H2O upto 25 μl

PCRs were performed with the following cycling conditions:



Annealing 55°C 30 s 18x

Extension 72°C 45 s


Primers used for cloning the expression constructs for HTRA1 variants for protein

expression in cell cultures are listed in Table 14.

3.2.3.2. Agarose gel electrophoresis

The PCR products were resolved by agarose gel electrophoresis. 1-1.5% agarose

was dissolved in TBE buffer by heating the solution in the microwave. After cooling


41 | P a g e

the liquid on ice it was mixed with two drops of ethidium bromide solution (AppliChem

GmbH, Darmstadt, Germany) and poured in a casting tray. The GeneRuler™ DNA

Ladder Mix (Fermentas International Inc., Burlington, Canada) served as a marker.

Samples were mixed before loading the gel with a drop of 5X DNA-loading buffer.

Electrophoresis was performed at 100-200 V in a chamber filled with TBE buffer.

Amplification products were visualized using the dark hood DH-40 (biostep GmbH,

Jahnsdorf, Germany). Gel runs were documented with the software ArgusX1 V4.0.81

(biostep GmbH, Jahnsdorf, Germany) and bands with the correct size were excised

from the gel.

3.2.3.3. DNA extraction from agarose gels

DNA extraction from agarose gels was performed with the NucleoSpin® Extract II Kit

(MACHEREY-NAGEL GmbH & Co. KG, Düren, Germany) according to the

manufacturer’s instructions. DNA was eluted in 20 μl of H2O.

3.2.3.4. Determination of DNA concentrations

DNA concentrations were determined in 2 μl of samples with the Nanodrop ND-1000

spectrophotometer using the Nanodrop ND-1000 v.3.5.2 software (Thermo Fisher

Scientific, Hudson, USA).

3.2.3.5. DpnI digestion

DpnI (New England BioLabs® Inc., Ipswich, USA) digestion of the amplified product of

HTRA1:CC variant (Section 3.2.3.1) was done. DpnI cut only the methylated

(adenine) recognition sequence 5’-GATC-3’ from the purified plasmid of “pGEM®-

T+HTRA1:CC”. Thus to get rid of the parent plasmid and proceed the further work

with only the unmethylated amplified product, DpnI digestion was carried out. For

digestion, the following components were mixed together in a cup and incubated for

2 h at 37°C.

Gel excised PCR product (50 ng/μl) 20 μl

DpnI 3 μl

NEBuffer CutSmart® 1 μl

H2O 6 μl


42 | P a g e

3.2.3.6. A-tailing of blunt-ended PCR fragments

The polymerases used for the amplifications (Section 3.2.3.1) generated blunt-ended

fragments because of their 3'-5'-exonuclease proofreading activity. However, for

subcloning PCR products into the pGEM®-T vector, they needed 3'-A-overhangs. The

purified PCR fragments were modified with the following A-tailing procedure: 7 μl of

the purified PCR products was mixed with 1 μl of 2 mM dATP, 1 μl of ThermoPol

Reaction Buffer (10X) and 0.5 μl of Taq DNA Polymerase (New England BioLabs®

Inc., Ipswich, USA) in a total reaction volume of 10 μl. The mixture was incubated at

72°C for 30 min.

3.2.3.7. Ligation into pGEM®-T vector

Next, subcloning of the PCR products with the pGEM®-T Vector System (Promega

Corporation, Madison, USA) occurred. Vector and A-tailed PCR products were added

in a molar ratio between 1:1 and 1:3 to the following ligation reaction.

2X Rapid Ligation Buffer 5 μl

T4 DNA Ligase 1 μl

pGEM®-T Vector 0.5 μl

Insert 1-3 μl

H2O upto 10 μl

The ligation was performed either for 1 h at RT or ON at 4°C.

3.2.3.8. Heat shock transformation of competent E.coli cells

For transformation of competent E.coli DH5α and JM110 cells, the heat shock

method was applied. 50 μl of DH5α cell suspension was thawed on ice and mixed

with 5 μl of the ligation reaction. After 30 min incubation on ice, the cells were heat

shocked for 1 min at 42°C. 5 min incubation on ice followed, before 800 μl of LB

medium (without antibiotics) was added to the suspension. During the recovery

phase, cells were incubated for 45 min at 37°C on a shaker. The bacteria were then

centrifuged at 4500 rpm in tabletop centrifuge for microfuge tubes (Heraeus Biofuge

Fresco centrifuge) for 3 min at RT and resuspended in 100 μl of LB medium. Finally,

cells were plated on LBAmp plates and incubated ON at 37°C.


43 | P a g e

For transformation with pGEM®-T vector, 40 μl of X-Gal (40 mg/ml dissolved in

DMSO) and 20 μl of 0.1M IPTG were additionally plated on LBAmp plates before

applying the E.coli cells.

3.2.3.9. Selection of positive clones

A helpful feature of the pGEM®-T vector is the ability to perform a so-called

“blue/white screening” of recombinant cells on LBAmp plates supplemented with X-Gal

and IPTG. The uptake site of the insert lies within the coding region of the α-peptide

of β-galactosidase, an enzyme that catalyzes the hydrolysis of β-galactosides into

monosaccharides. The organic compound X-Gal is also cleaved by β-galactosidase,

leading to the accumulation of an insoluble blue product. Bacteria harboring the

pGEM®-T vector without an insert express β-galactosidase after IPTG induction. The

colonies will appear blue. However, if an insert is ligated into the vector, the

transformed cells do not express a functional β-galactosidase protein. They are

unable to cleave X-Gal and the colonies will remain white. It should be mentioned

that the E.coli strain DH5α used for transformation does not express a functional

copy of this gene. Sixteen white clones were picked and inoculated ON into 5 ml

LBAmp medium for subsequent plasmid preparation and sequencing.

3.2.3.10. Plasmid isolation

For sequencing and restriction digestion, plasmids were isolated with the

NucleoSpin® Plasmid Kit (MACHEREY-NAGEL GmbH & Co. KG, Düren, Germany)

from 4 ml of ON cultures. Isolation was performed according to the manufacturer’s

instructions. DNA was eluted in 50 μl of H2O and the concentration was determined

with the Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Hudson,

USA).

For transfection of mammalian cell lines, plasmids were isolated with the

NucleoBond® Xtra Midi Kit (MACHEREY-NAGEL GmbH & Co. KG, Düren, Germany)

from 100 ml of ON cultures. DNA pellets were reconstituted in 100 μl of H2O and

diluted to a final concentration of approximately 1 μg/μl.


44 | P a g e

3.2.3.11. Cycle sequencing of inserts

The correctness of the insert’s sequence was determined by cycle sequencing with

the BigDye® Terminator v1.1 Cycle Sequencing Kit (Applied Biosystems Inc., Foster

City, USA). Sequencing reactions were performed with the primers mentioned in

Table 14. The following components were pipetted on ice:

BigDye® Terminator Sequencing Buffer (5X) 2 μl

Primer (10 µM) 1 μl

BigDye® Terminator v1.1 0.8 μl

Plasmid DNA 50-100 ng

H2O upto 10 μl

The cycling conditions were set as follows:



Annealing 58°C 30 s 27 x

Extension 60°C 3 min


The DNA was then precipitated with 2 μl of 3M sodium acetate and 25 μl of 100%

ethanol at 2800 rpm for 20 min at RT in centrifuge for Falcon tubes (Heraeus

Megafuge 1.0R centrifuge). After washing with 50 μl of 70% ethanol and subsequent

centrifugation, the DNA was briefly air-dried, resuspended in 15 μl of Hi-Di™

Formamide (Applied Biosystems Inc., Foster City, USA) and transferred to a 96-well

sample plate. Sequencing was performed with the 3130xl Genetic Analyzer capillary

sequencer (Applied Biosystems Inc., Foster City, USA). The outputs were aligned to

the expected sequences with BioEdit Sequence Alignment Editor v7.0.9.0 (Tom Hall,

Ibis Therapeutics, Carlsbad, USA).

3.2.3.12. Restriction digestion of correct inserts and ligation into target vectors

Restriction sites compatible with the multiple cloning sites of the pCEP4 vectors

containing “TC-tagged HTRA1:CG” and “TC-tagged HTRA1:TT” were already


45 | P a g e

introduced in the primers used for amplifying the fragments, which were first

subcloned into the pGEM®-T vector. The enzymes NotI and XhoI were used for the

restriction digestion of the TC-tagged HTRA1 variants cloned for protein expression

in cell culture. To obtain pCEP4 containing TC-tagged HTRA1:CC variant from its

subclone of pGEM®-T vector, FseI enzyme was used to digest. It is to be noted that,

for restriction digestion by FseI (a dcm methylation sensitive enzyme), pGEM®-T

vector containing TC-tagged HTRA1:CC variant were transformed into dam-/dcm-

competent E.coli JM110 cells. After ligation and obtaining pCEP4 vector containing

TC-tagged HTRA1:CC variant, the plasmids were transformed into DH5α cells. To

obtain expression constructs for Strep-tagged HTRA1:CC variant, pCEP4 construct

for TC-tagged HTRA1:CC (construct obtained as described in Section 3.2.3.1) was

digested by FseI. The digested fragment was inserted into pEXPR-IBA103

(consisting of Twin Strep tag) construct for HTRA1:CG variant (already available in

Institute of Human Genetics, University Hospital Regensburg, Regensburg,

Germany). The pEXPR-IBA103 construct for HTRA1:CC variant was transformed into

DH5α cells.

pGEM®-T plasmids (2-3 μg) carrying a correct insert and the target vectors (2-3 μg)

were digested at least for 1 h at 37°C. 1 μl of each restriction enzyme from New

England BioLabs® Inc. (Ipswich, USA) was used. The NEBuffer CutSmart® was

chosen according to the supplier’s recommendations. After restriction digestion

fragments were resolved on agarose gels and the fragment of interest was excised

from the gel. DNA purification was performed according to Section 3.2.3.3.

Next, ligation into the target vectors was performed. Similar to ligations into pGEM®-

T, vector and insert were added in a molar ratio between 1:1 and 1:3 in the following

ligation assay:

T4 DNA Ligase Reaction Buffer (10X) 5 μl

T4 DNA Ligase 1 μl

Vector 50-100 ng

Insert (molar ratio vector:insert) 1:1 to 1:3

H2O ad 10 μl


46 | P a g e

The ligation was performed either for 1 h at RT or ON at 16°C.

Transformation, selection of positive clones, plasmid isolation and cycle sequencing

were accomplished as described in the previous sections.

3.2.3.13. Long-term storage of positive clones

Positive clones were maintained as glycerol stocks for long-term storage. To this end,

bacteria from 4 ml of fresh overnight culture were pelleted at 4000 rpm in centrifuge

for Falcon tubes for 3 min at RT. Cells were then resuspended in 500 μl of LB medium

(without antibiotics) and mixed with 600 μl of sterile 80% glycerol. The stocks were

immediately frozen at 80°C.

3.2.4. Transfection of Hek293-Ebna cell lines

For introducing TC-tagged and Strep-tagged HTRA1 expression constructs in

Hek293-Ebna cells, transfection was performed with the TransIT®-LT1 Transfection

Reagent (Mirus Bio LLC, Madison, USA), a broad spectrum protein/polyamine-based

reagent that contains histones and lipids. According to manufacturer’s indications,

optimal transfection efficiency is reached at a cell confluence of approximately 50-

70%. Transfection of cells grown in a 100 mm dish was performed after 24 h as

follows: 10 µg of plasmid DNA was diluted and vortexed in 1 ml of the respective

growth medium without supplements (Table 20). Subsequently 30 µl of TransIT®-LT1

transfection reagent was added to the tube, which was gently vortexed again. During

the incubation time of 15-20 min at RT, transfection reagent-DNA complexes were

formed. The mixture was finally added dropwise to the cells and the culture plate was

gently shaken and incubated under standard conditions for 48 h.

3.2.5. Secretion assay

To analyze HTRA1 secretion from Hek293-Ebna cells, cells were seeded ON into 6-

well plates using 3 ml of Opti-MEM® I Reduced Serum Media (Gibco Life

Technologies). At 70% confluence, the cells were transfected with 2.5 µg of the

expression constructs for untagged HTRA1 (HTRA1:CG, HTRA1:TT or empty vector)

and 7.5 µl TransIT-LT1 transfection reagent (Mirus Bio LLC) following the


47 | P a g e

manufacturer’s instructions. Supernatants and cells were harvested 0, 10, 16, 20 and

24 h after transfection (see Section 3.2.6). Proteins isolated were subjected to

Western blot analyses against α-HTRA1 and α-ACTB antibodies as described in

Section 3.2.9.

3.2.6. Preparation of protein samples for gel loading

Supernatants were collected in 15 ml Falcon tubes (Sarstedt AG, Nürnbrecht,

Germany) from the top of cells. Supernatants were centrifuged at 1000 rpm in

centrifuge for Falcon tubes for 3 min at RT to get rid of the debris. Clear supernatants

were collected and the total protein concentration was measured by Bradford assay

(see Section 3.2.7). Cells from 100 mm dishes were washed twice with 1X PBS and

were then resuspended in 1X PBS. The cells in PBS were then transferred into a 15

ml Falcon tube and centrifuged for 10 min at 4000 rpm in the centrifuge for Falcon

tubes. The cell pellet was resuspended in 1 ml 1X PBS, transferred into a 1.5 ml

Eppendorf cup and centrifuged for at 4000 rpm in tabletop centrifuge (for microfuge

tubes) for 10 min at RT and resuspended in 750 μl of 1X PBS. Total protein

concentration of the cells was then measured.

3.2.7. Bradford assay for measurement of protein concentration

To find out the total protein concentration of cells or supernatants, according to

Bradford assay principle, Roti® Quant solution was used. 10-50 µl of resuspended

cells or supernatant was added to a mixture of 200 µl of Roti® Quant solution and 800

µl H2O (Millipore). To calibrate the photometer, a sample was prepared from 800 µl

H2O and 200 µl Roti® Quant (without protein), which is termed as “Blank”. The

samples were incubated for 20 min at RT and then the optical density (OD) was

measured at 595 nm in a photometer. The determination of each protein

concentration was always done in triplets. The total protein concentration of cells or

supernatants was then analyzed by comparing with the OD of the titration series of

known concentration of a protein such as bovine serum albumin (BSA). The total

protein concentration of each protein in each experiment was equalized before

subjecting to SDS-PAGE.


48 | P a g e

3.2.8. SDS PAGE (Sodium Dodecyl Sulfate-Polyacrylamide Gel

Electrophoresis)

12.5 µl of 5X Laemmli buffer was added to 50 µl of each protein sample and heated

for 10 min at 95°C before loading onto SDS PAGE.

15% SDS-polyacrylamide gels were poured into the Mini Protean 3 setup (Bio-Rad

Laboratories Inc., Hercules, USA) using the following mixtures:

Resolving gel (15%)

3.75 ml Rotiphorese Gel 40 (29:1) acrylamide/bisacrylamide

3.38 ml 1 M Tris-HCl pH 8.8 2.42 ml H2O

100 µl 20% SDS

100 µl 10% APS

10 µl TEMED

Stacking gel (3%)

0.55 ml Rotiphorese Gel 40 (29:1) acrylamide/bisacrylamide 2.76 ml 1 M Tris-HCl pH 6.8

1.69 ml H2O 50 µl 20% SDS 50 µl 10% APS 5 µl TEMED

The gels were transferred to a chamber containing SDS-running buffer and were

loaded with 25 µl of sample per lane. 4 µl of the PageRuler™ Prestained Protein

Ladder (Fermentas International Inc., Burlington, Canada) was used as marker. The

running program was first set at 50 V for approximately 1 h until the samples entered

the resolving gel and then at 150 V until the bromophenol blue front (contained in

Laemmli buffer) reached the lower end of the gel.

3.2.9. Western blot (WB)/Immunoblot (IB)

For Western blot analyses proteins were blotted after SDS-PAGE in the semi-dry

procedure onto a PVDF membrane. The membrane was activated for 30 s in

methanol, and then equilibrated for at least 15 min in 1X Towbin transfer buffer.

Likewise, two 3 mm Whatman Paper and the SDS gel were equilibrated in 1X Towbin

transfer buffer. The transfer of the proteins from the gel to the membrane was


49 | P a g e

performed for 40 min at 24 V. After transfer, the membrane was pivoted in block

solution for 1 h at 4°C, before it was incubated ON in the primary antibody. Before

incubation in secondary antibody (1 h, 4°C), the membrane was washed three times

for 10 min in 1X PBS. The secondary antibody (Horseradish Peroxidase conjugated)

was added and the membranes were incubated for at least 1 h at 4°C. The

membranes were washed again three times with 1X PBS for 10 min.

The protein-antibody complexes were visualized by chemiluminescence using the

SuperSignal West Femto Maximum Sensitivity Substrate (Thermo Fisher Scientific,

Hudson, USA) according to the manufacturer’s instructions. The blot was exposed to

CRONEX™ 5 X-ray film (Agfa-Gevaert N.V., Mortsel, Belgium). The films were finally

developed with the G153 developer and G354 fixer (Agfa-Gevaert N.V., Mortsel,

Belgium). All antibodies used are enlisted in Table 10.

3.2.10. MicroScale Thermophoresis (MST) to study conformation of

HTRA1 isoforms

3.2.10.1. In-Gel TC-tagged HTRA1 detection

In-Gel detection of the TC-tagged HTRA1 was carried out to identify the optimal

concentration of FlAsH EDT2 fluorescence labeling reagent (Invitrogen, Carlsbad,

CA, USA) needed for the TC-tagged HTRA1 to exhibit fluorescence without

background.

Hek293-Ebna cells were transfected as described inSection 3.2.4. After 48 h of

transfection, the supernatant of Hek293-Ebna cells transfected with empty pCEP4

vector, expression vectors for TC-tagged HTRA1:CG or HTRA1:TT variants were

harvested as described in Section 3.2.6 and concentrated with Amicon Ultra 10,000

Kw 4 ml columns (Merck, Millipore, Billercia, MA). 30 µl of concentrated protein was

mixed with 10 µl of 4x loading dye (Laemmli buffer) without β-Mercaptoethanol and 1

µl of 80 mM TCEP. The mixture was heated at 70°C for 10 min. The mixture was

cooled down to RT. 1 µl of 200 µM FlAsH EDT2 in DMSO was added to the mixture.

This mixture was incubated for another 10 min at RT. 40 µl of respective protein in

15% SDS Polyacrylamide Gel was run at 150 V for 1 h 20 min. The fluorescent


50 | P a g e

protein bands were visualized in Fujifilm FLA-5000 image scanner (Fujifilm,

Dusseldorf, Germany).

3.2.10.2. The temperature-dependent structural assays by MST

MST analyses were performed by Dr. Thomas Schubert, 2bind GmbH, Regensburg,

Germany. Total protein concentration of the concentrated supernatant of the cells

transfected with expression vectors containing TC-tagged HTRA1 CC, CG, or TT

variants was measured by Bradford assay as described in Section 3.2.7. 4 µl of the

concentrated supernatants (2 µg/µl) were incubated with 1 µl of 20 µM of FlAsH-

EDT2. Samples were incubated for 10 min and sucked into the standard capillaries.

The capillaries were plugged with paraffin to avoid evaporation. The temperature-

dependent structural assays were performed as biological triplicates at 15% LED

(light-emitting diode) power and 50% MST power in a Monolith NT.115 (NanoTemper

Technologies, Munich, Germany) with varied temperatures (32-52 °C). The recorded

fluorescence of each protein was normalized to the same baseline fluorescence and

plotted against the temperature into one graph using KaleidaGraph 4.1.

3.2.11. Purification of Strep-tagged variants of the HTRA1

For purification of the secreted HTRA1 isoforms, 10 ml supernatants of Hek293-Ebna

cells transfected with expressions constructs for Strep-tagged HTRA1: CG, CC or TT

variants were collected in Falcon tubes. To get rid of cellular debris, supernatants

were centrifuged at a speed of 1000 rpm in centrifuge for Falcon tubes for 3 min at RT.

The purification of heterologously expressed HTRA1 was carried out with the Twin

Strep Purification Kit from IBA Life Sciences, Göttingen, Germany. 1 ml Gravity flow

Strep Tactin Superflow® columns were used. W buffer, pH 8.0 (wash buffer) was

prepared in accordance with Table 4. The buffer E (elution buffer) and R

(regeneration buffer) were purchased from IBA Life Sciences.

The purification of the proteins was carried out at 4°C with pre-cooled solutions and

buffers according to the Short Purification Protocol of IBA Life Sciences. Elution of


51 | P a g e

the proteins was carried out in five steps with 500 µl buffer E. The eluate fractions

were stored in aliquots of 100 µl at -20°C.

3.2.12. Coomassie staining

The Coomassie staining of SDS gels was carried out directly following the SDS-

PAGE. SDS-gels were stained in Comassie Stainer for 1 h and then destained in

Coomassie destainer till the distinct protein bands were visible. The composition of

the solutions was mentioned in Table 4.

3.2.13. Casein digest to test bio-activity of HTRA1 protein

Bioactivity of HTRA1 protein from supernatant or purified HTRA1 was tested by in

vitro β-casein digestions. The casein solution used herein contains a casein mixture

of α-S1 (22 kDa), α-S2 (25 kDa), β (24 kDa), γ (70 kDa) and κ (19 kDa). HTRA1 is

capable of digesting β-casein. 20 µg of casein from bovine milk (Merck Chemicals

GmbH, Schwalbach, Germany) was mixed with 2 µg purified HTRA1 isoforms in 100

µl of digestion buffer pH 7.5 (see Table 4 for composition). After 0 and 3 h, 50 µl

aliquots were taken and the reaction was stopped by adding 5X Laemmli buffer and

boiling the sample for 10 min at 90 °C. Samples were resolved by SDS PAGE and

stained with Coomassie.

3.2.14. Limited partial proteolysis

25 µl of 40 ng/µl of Strep-purified HTRA1 in 1X elution buffer along with 50 µl of TRIS

buffer saline (see Table 4 for composition) was preincubated at 37, 42 and 46°C for

10 min. 25 µl of 480 µg/ml of TPCK-Trypsin (Sigma-Aldrich, St. Louis, MO, USA) in

TRIS buffer saline was added to denatured HTRA1 at 37°C for another 5 min. 25 µl

of 5X Laemmli buffer was added to the reaction mixture and boiled. Subsequently,

samples were subjected to Western blot analyses with α-HTRA1 antibody.

3.2.15. MST interaction analysis

MST binding experiments were carried out with 100 nM labeled HTRA1 in

supernatant with varied concentrations of recombinant TGF-β1 (PeproTech,


52 | P a g e

Hamburg, Germany) and β-casein from bovine milk (Sigma-Aldrich, St. Louis, MO,

USA) at 80% MST power, 20% LED power in standard capillaries on a Monolith

NT.115 at 25°C. The recorded fluorescence was normalized to the fraction of the

protein bound (0 = unbound, 1 = bound) and processed with KaleidaGraph 4.1

software. The data were fitted with the help of the quadratic fitting formula (Kd

formula) derived from the law of mass action. Each binding experiment was done with

biological triplicates. MST interaction analyses were performed by Dr. Thomas

Schubert.

3.2.16. TGF-β1/β-casein in vitro digestion

TGF-β1 (PeproTech) was dissolved in 10mM citric acid pH 3.0 to make a stock

concentration of 100 ng/µl. 10 µl from stock, i.e., 1 µg of TGF-β1 was added to 90 µl

of digestion buffer pH 7.5 (see Table 4 for composition). 1 µg of TGF-β1 was

incubated with 300 µl serum-free medium of Hek293-Ebna cells transfected with

expression constructs for HTRA1:CG, HTRA1:TT or control (empty pCEP4 vector) for

24 h. After 0, 4, 8, 16 and 24 h, 50 µl aliquots were taken from respective

supernatants. To 50 µl of aliquots, 12.5 µl of 5X Laemmli buffer was added and

boiled to stop the reaction.

In vitro digestion of β-casein by HTRA1 was analyzed by Karolina Ploessl, Institute of

Human Genetics, University Hospital Regensburg, Regensburg, Germany. To

compare β-casein cleavage catalyzed by HTRA1:CG and HTRA1:TT, 20 µg of β-

casein was incubated with 300 µl serum-free medium of Hek293-Ebna cells

transfected with expression constructs for HTRA1:CG, HTRA1:TT or control (empty

pCEP4 vector). After 0.5, 1, 2 and 3 h, 50 µl aliquots were taken and the reaction was

stopped by adding 5X Laemmli buffer and by boiling the sample for 10 min at 90°C.

Samples were resolved by SDS PAGE and stained with Coomassie.

3.2.17. MLEC luciferase assay

MLEC-PAI/Luc cells were seeded into 96-well plates at a density of 1.5 × 104 cells per

well with 100 µl of appropriate medium with growth supplements (Table 20). Cells


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were allowed to attach for 3 h at 37°C in 5% CO2 incubator. The cells were treated

with 2 ng/ml of recombinant TGF-β1 (PeproTech) in combination with 40 ng/ml of

purified HTRA1 (Strep-tagged HTRA1:CG or HTRA1:TT) or eluate of empty vector.

After 16 h, cultures were centrifuged at a speed of 2500 rpm centrifuge for Falcon

tubes for 6 min at RT. After washing with 1 ml of PBS, cells were resuspended in 500

µl of Reporter Lysis Buffer and incubated for 15 min at RT. Cellular debris was then

pelleted at a speed of 8000 rpm in tabletop centrifuge (for microfuge tubes) for 3 min

at RT, and 350 µl of the supernatant was transferred to a fresh tube. 10 µl of cell

lysate was assayed for luciferase activity with 100 µl of Luciferase Assay Reagent

(Promega Corporation, Madison, USA), which contains the luciferase’s substrate.

Luminescence was measured in the FLUOstar OPTIMA plate reader (BMG LABTECH

GmbH, Offenburg, Germany) for 15 s upon injection of the reagent.

3.2.18. Treatment of BV-2 cells with BV-2-conditioned medium and

HTRA1

The procedure for inducing endogenous TGF-β signaling in BV-2 cells was followed

in concordance with Spittau et al. (2013). For this treatment, two sets of BV-2 cells

were seeded.

The first set of BV-2 cells were seeded at a density of 1.5 ×105 in 12-well cell culture

plates with 1.5 ml of appropriate medium with serum and growth supplements (for

immunocytochemistry) and at a density of 3 ×105 in 6-well cell culture plates with 3 ml

of appropriate medium with serum and growth supplements (for Western Blot

analyses and Pai-1 gene expression). Cells were allowed to attach and reach the

subconfluence for 6 h at 37°C in 5% CO2 incubator. After 6 h, medium with serum

and growth supplements was replaced by equal volume of serum-free medium. Cells

were serum-starved for 24 h. After 24 h microglia-conditioned medium from this set of

BV-2 cells was collected, cellular debris was removed by centrifuging in centrifuge for

Falcon tubes in (cell culture) at 1000 rpm for 3 min at RT.

The second set of BV-2 cells was seeded similarly and was allowed to attach and

grow to subconfluence for similar time and similar condition as for the first set.

Instead of 24 h, these cells were kept serum-starved for 2 h. After 2 h, the serum-free

medium was removed. The harvested microglia-conditioned medium of the first set of


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BV-2 cells was transferred into the second set of BV-2 cells as shown in Figure 8.

The treatment was continued with the second set of BV-2 cells in the conditioned

medium.

Figure 8: Schematic overview of inducing autocrine TGF-β/SMAD signaling in BV-2 cells. Adapted and modified from Spittau et al. (2013).

For, SMAD signaling analysis, 40 ng/ml of Strep-tagged HTRA1:CG and HTRA1:TT

eluates (3 µl of 40ng/µl HTRA1 eluates and equal volume of Strep-tagged empty

eluate was added to 3 ml of media in six-well plates and 1.5 µl of 40ng/µl HTRA1

eluates and equal volume of Strep-tagged empty eluate was added to 1.5 ml of

media in 12-well plates) was added to the BV-2 cells in the conditioned media for 2 h.

After 2 h, BV-2 cells in 6-well plates were harvested for protein isolation.

Subsequently, the proteins were subjected to Western blot analyses (Section 3.2.9)

with SMAD and pSMAD2 antibodies (Table 10). Immunocytochemistry for pSMAD2

antibody was performed by Magdalena Schneider, Institute of Human Anatomy and

Embryology, Faculty of Biology and Preclinical Medicine, University of Regensburg,

Regensburg, Germany, as described in (Friedrich et al., 2015)

For relative Pai-1 gene expression, BV-2 cells (in conditioned medium) in 6-well

plates were harvested after 3 h and 24 h of treatment with Strep-tagged HTRA1

eluates (same concentration as above). The cells were harvested for RNA isolation

and cDNA treatment (see below). The relative gene expression analysis via

Quantitative Real-Time PCR (qRT-PCR) was done by Magdalena Schneider as

described in (Friedrich et al., 2015).


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3.2.19. BV-2 cells treatment with lipopolysaccharide (LPS) and HTRA1

BV-2 cells were seeded at a density of 3 ×105 in 6-well plates with 3 ml of media. BV-

2 cells were serum-starved for 24 h before any treatment. To identify an effect of

HTRA1 on classical activation of microglia, cells were first seeded and serum-starved

as described before. BV-2 cells were treated with 50 ng/ml LPS for 24 h, (as

described in Dirscherl et al., 2010; Karlstetter et al., 2014; Aslanidis et al., 2015) in

presence of increasing HTRA1 concentrations (30 ng/ml, 60 ng/ml and 90 ng/ml).

After 24 h, cells were harvested for RNA isolation followed by relative gene

expression analysis and the supernatant was collected for NO assay.

3.2.20. BV-2 cells treatment with interleukin 4 (IL4), TGF-β1 and HTRA1

BV-2 cells were seeded at a density of 3 ×105 in 6-well plates in 3 ml of appropriate

medium. The cells were serum-starved for 24 h before any treatment, as described

before. BV-2 cells were treated separately or in combination with 1 ng/ml of TGF-β1

and 10 ng/ml of IL4 for 24 h (as described in Zhou et al., 2012) in presence of

increasing HTRA1 concentrations (30 ng/ml, 60 ng/ml and 90 ng/ml). Cells were

harvested after 24 h for RNA isolation and relative gene expression analysis.

3.2.21. Nitrite measurement by nitric oxide (NO) assay

After 24 h, supernatant of LPS- and HTRA1-treated BV-2 cells were harvested.

Supernatant was centrifuged at a speed of 8000 rpm in tabletop centrifuge (for

microfuge tubes) for 5 min to get rid of cellular debris. Nitrite measurement was

carried out with the clear supernatant as per manufacturer’s instruction (Griess

Reagent System, Promega Corporation, Madison, USA). The colorimetric assay was

measured in Magellan™ Plate Reader (TECAN US, Durham, NC, USA) (available at

Institute of Clinical Chemistry, University Hospital Regensburg, Regensburg,

Germany) according to manufacturer’s instructions. This assay is also known as NO

assay.


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3.2.22. RNA analysis

3.2.22.1. RNA isolation from cell cultures

Total RNA was isolated from cell cultures with the RNeasy® Mini Kit (Qiagen N.V.,

Hilden, Germany). Before isolating RNA from BV-2 cell lines, they were washed twice

in PBS. Cells were harvested with a sterile scraper in 600 μl of RLT buffer with 1% β-

mercaptoethanol, freshly added before use. Complete cell wall disruption and

homogenization was achieved by passing the suspension 20 times through a blunt

20-gauge needle fitted to a 1 ml syringe. Subsequently, 600 μl of RNase-free 70%

ethanol was added to the lysate. Further steps were performed according to the

manufacturer’s protocol including the on-column DNase digestion with the RNase-

Free DNase Set (Qiagen N.V., Hilden, Germany). RNA concentrations were

determined in 2 μl of samples with the Nanodrop ND-1000 spectrophotometer using

the Nanodrop ND-1000 v.3.5.2 software (Thermo Fisher Scientific, Hudson, USA).

After RNA elution in 50 μl of H2O, samples were permanently kept on ice and fast

processed or stored at -80 °C.

3.2.22.2. First strand cDNA synthesis from RNA

For first strand cDNA synthesis from RNA the RevertAid™ M-MuLV Reverse

Transcriptase enzyme (Fermentas International Inc., Burlington, Canada) was used.

cDNA synthesis was performed with 500-1000 ng of RNA template and random

primers. As a first step, pre-incubation of RNA for 5 min at 65°C with 1 μl of primers

(100 pmol) was carried out in 12.5 µl reaction volume, adjusted with RNase-free H2O.

Afterwards the following components were added on ice:

RevertAid™ 5X Reaction Buffer (5X) 4 μl

dNTPs(10 mM each) 2 μl

RevertAid™ M-MuLV Reverse Transcriptase 1 μl

H2O(Millipore) 0.5 µl

The reaction mixture was then incubated for 10 min at 25°C, followed incubation at

42°C for 60 min. The reaction was stopped by heating the mixture to 70°C for 10

mins.


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3.2.22.3. qRT-PCR

qRT-PCR was performed for each sample in triplicates. The cDNA was diluted for the

qRT-PCR to a concentration of 20 ng/µl. The primer and probe design of all primers

was carried out via the "Universal Probe Libary" (Hoffmann-La Roche). The PCR

reactions as shown below were pipetted in a 384-well plate. The PCR was performed

according to the PCR program shown below using the ViiA™ system 7(Life

Technologies) machine. The analysis was performed according to the ΔΔCt method.

Relative gene expressions of classical (M1) microglial activation markers namely

interleukin 6 (IL6) and inducible nitric oxide synthase (iNOS) or alternative (M2)

microglial activation markers namely arginase 1 (Arg1) and chitinase 3-like 3 (Ym1)

were analyzed by normalizing the expression to mouse ATPase, a housekeeper

gene. All primers and probes used for these experiments are enlisted in Table 16.

The statistical significance of the test results was determined in Microsoft Excel with a

Student's t-test. Reaction mixture for qRT-PCR for Arg1, iNOS, IL6, YM1 and mouse

ATPase are as follows:

TaqMan Gene Expression Master Mix(MM)(2X) 5 μl



Roche Probe 2 μl

cDNA 5 µl

H2O(Millipore) 0.375 µl

Following cycling conditions were set for qRT-PCR:

Denaturation 95°C 40 s 1X

Annealing 60°C 60 s 1X

Extension 60°C 2 min 40X

RESULTS

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4. RESULTS

4.1. Cloning and expression of HTRA1 variants

4.1.1. HTRA1 haplotypes applied in subsequent studies

Three expression constructs for HTRA1 were generated including HTRA1 exon 1

variants rs1049331:C and rs2293870:G, the reference haplotype (Fritsche et al.,

2008) (referred to as HTRA1:CG), the AMD-associated variants rs1049331:T and

rs2293870:T (HTRA1:TT), and the non-disease associated variants rs1049331:C and

rs2293870:C (HTRA1:CC) (Figure 9).

Figure 9: Schematic diagram of relative positions of synonymous polymorphisms in exon 1 of HTRA1. This scheme is used to illustrate HTRA1 haplotypes chosen for generating expression constructs for HTRA1 variants; HTRA1:CG and HTRA1 CC are not associated with AMD risk, HTRA1:TT is associated with increased AMD risk. Figure adapted from Friedrich et al. (2011).

4.1.2. HTRA1 expression constructs

Expression constructs for untagged HTRA1 variants in pCEP4 vector (already

available at Institute of Human Genetics, University Hospital Regensburg, Germany)

were used for analyzing secretion and intracellular accumulation of HTRA1 isoforms

(Figure 10A). Expression constructs for Strep-tagged HTRA1 variants were used for

purifying HTRA1. HTRA1:CG and HTRA1:TT variants (in pEXPR-IBA103) were

already available at Institute of Human Genetics, University Hospital Regensburg,

Germany. Strep-tagged HTRA1:CC variant was generated within this study (Figure

RESULTS

59 | P a g e

10B). For MST analyses, the three HTRA1 variants were C-terminally fused to a

Tetracystein (TC)-motif (Cys-Cys-Pro-Gly-Cys-Cys), allowing labeling with FlAsH-

EDT2 to show fluorescence. (Figure 10C).

Figure 10: Schematic representation of expression constructs for (A) untagged, (B)TC-tagged and (C)Strep-tagged HTRA1 variants. Vector backbones used for heterologous expression of HTRA1 variants are depicted: pCEP4 (Figure modified and adapted from www.invitrogen.com) was used for heterologous expression of (A) untagged HTRA1, as well as (B) TC-tagged HTRA1 variants. pEXPR-IBA103 (Figure modified and adapted from www.iba-lifesciences.com) was used for generating (C) Strep-tagged HTRA1 variants. Restriction sites used for inserting HTRA1 variants are labeled within the figures.

4.1.3. Characterization of HTRA1 expression constructs

Before performing experiment with heterologously expressed HTRA1 variants, the

expressed proteins were subjected to quality control by Bradford Assay, Western Blot

and casein in vitro digest (Figure 11). Cells transfected with expression constructs for

HTRA1 variants were harvested. The total protein concentration was measured by

using Bradford assay and equal amount of respectively tagged or untagged proteins

were subjected to Western blot to compare the expression of both AMD non-risk- and

risk-associated HTRA1 isoforms (exemplarily shown for HTRA1:CG and HTRA1:TT

in Figure 11). 300 µl of the supernatants were incubated with casein for 3 h to test for

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HTRA1 bioactivity (exemplarily shown for HTRA1:CG and HTRA1:TT in Figure 11),

as described in (Murwantoko et al., 2004; Vierkotten et al., 2011; Eigenbrot et al.,

2012).

Figure 11: Characterization of HTRA1 expression and bioactivity. Hek293 cells were transfected with expression constructs for HTRA1:CG and HTRA1:TT variants. These HTRA1 variants were either untagged (A and D) or TC-tagged (B and E) or Strep-tagged (C and F). Immunoblots were performed with cell lysates using α-HTRA1 antibodies. The β-actin (ACTB) immunoblot served as loading control. For analyzing bio-activity of untagged and TC-tagged HTRA1 isoforms (D and E) respectively, 300 µl of serum-free supernatants were mixed with 20 µg of β-casein and incubated at 37°C over a period of 3 h. Samples were taken after 0 h and 3 h and subjected to SDS-PAGE with subsequent Coomassie staining. For analyzing bio-activity of Strep-tagged HTRA1 (F), first 10 ml of serum-free supernatants of Hek293 cells transfected with expression constructs of Strep-tagged HTRA1 variants were used to purify the HTRA1 isoforms in Strep Tactin Superflow® columns according to manufacturer’s instructions. After purification 5 µg of each purified Strep-tagged HTRA1 isoform was mixed with 20 µg of β-casein and incubated at 37°C over a period of 3 h. Samples taken after 0 h and 3 h were subjected to Coomassie staining and analyzed as described above.

The bio-activity of HTRA1:CG and HTRA1:TT isoforms did not show any significant

difference. This result was consistent whether the isoforms were untagged, TC-

tagged or Strep-tagged.

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4.2 Influence of synonymous SNPs within HTRA1 exon 1 on protein

structure

To assess protein folding properties of the different HTRA1 haplotypes, MST, a

method which determines the movement of molecules along a temperature gradient,

was applied (Duhr and Braun, 2006; Baaske et al., 2010). The thermophoretic

movement is sensitive to changes in the molecular structure or conformation, thus

allowing detection of minimal alterations of protein variants or complexes.

For monitoring purposes, a C-terminal tetracysteine (TC)-tag motif was fused to each

of the haplotype constructs providing a specific fluorescent labeling of the translated

protein (Adams et al., 2002; Madani et al., 2009). The TC-tagged haplotype

constructs were then fused into pCEP4 expression vector (Invitrogen, Carlsbad,

USA). Therefore, TC-tagged HTRA1 variants were heterologously expressed in

Hek293 cells. After 72 h, supernatants were harvested, TC-tagged HTRA1 isoforms

within the supernatants were labeled by a fluorescent dye and MST analyses were

performed with the labeled HTRA1 isoforms.

4.2.1. Preparation and adjustment of TC-tagged HTRA1 isoforms

After harvesting the supernatant from Hek293 cells transfected with expression

vectors containing the different TC-tagged HTRA1 variants, the supernatants were

concentrated by Amicon Ultra 10,000 Kw 4 ml columns and the total protein

concentrations were measured by Bradford assay. The protein concentration of all

supernatants containing TC-tagged HTRA1 isoforms were adjusted to 2 µg/µl and

subjected to Western blot.

Figure 12: Adjustment of HTRA1 concentrations in supernatant of Hek293 cells transfected with expression vectors containing TC-tagged HTRA1 variants. (A) Supernatants of transfected Hek293 cells with expression vectors for TC-tagged variants or control (empty expression vector) were adjusted to a protein concentration of 2 µg/µl and subjected to Western blot analyses using α-HTRA1 antibodies. (B) Densitometry analysis of relative HTRA1 protein levels from (A), calibrated against measurements for HTRA1:CG. Data represent the mean +/- SD of three independent immunoblots before each independent MST analyses.

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Each supernatant of Hek293 cells transfected with expression constructs for either

TC-tagged HTRA1:CG, HTRA1:CC or HTRA1:TT variants showed similar HTRA1

protein level (Figure 12).

4.2.2. Labeling TC-tagged HTRA1 with FlAsH-EDT2 for MST analyses

The biarsenical labeling reagent FlAsH-EDT2, used in bioanalytical research,

becomes fluorescent upon binding to TC-tagged recombinant proteins containing the

tetracysteine (TC) motif (Adams et al., 2002; Adams and Tsien, 2008). In order to

label TC-tagged HTRA1, the supernatants were incubated with 5 µM concentration of

FlAsH-EDT2 according to manufacturer’s instructions. The labeled samples were

subjected to SDS-PAGE and the image was captured by Fujifilm FLA-5000 image

scanner (available at the Institute of Microbiology, Faculty of Biology and Preclinical

Medicine, University of Regensburg, Germany).

Figure 13: Labeling TC-tagged HTRA1 with FlAsH-EDT2 for MST analyses. The following samples were loaded (from left to right): Medium: Serum-free medium for Hek293 cells; FlAsH-EDT2: 5 µM FlAsH-EDT2 labeling reagent; HTRA1:CG: TC-tagged HTRA1:CG labeled with 5 µM FlAsH-EDT2 in supernatant of Hek293 cells transfected with expression construct for TC-tagged HTRA1:CG variant; HTRA1:TT: TC-tagged HTRA1:TT labeled with 5 µM FlAsH-EDT2 in supernatant of Hek293 cells transfected with expression construct for TC-tagged HTRA1:TT variant; and control: supernatant of Hek293 cells transfected with empty vector, labeled with 5 µM FlAsH-EDT2.

Figure 13 illustrates that 5 µM concentration of FlAsH-EDT2 specifically binds to TC-

tagged HTRA1:TT and CG isoforms but did not give any signal in the control

(supernatant of cells transfected with empty pCEP4 vector). The free dye also does

not show any fluorescence.

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4.2.3. HTRA1:CG, HTRA1:TT and HTRA1:CC protein conformation

comparison by MST

Adjusted and labeled HTRA1 variants in supernatants were subjected to MST

analyses by increasing the temperature gradually from 32°C to 52°C, with an interval

of 2°C. The thermophoretic mobility of each HTRA1 isoform at the given temperature

was recorded in Monolith NT.115 (NanoTemper Technologies, Munich, Germany).

MST analyses were carried out by Dr. Thomas Schubert, 2bind GmbH,

Regensburg,Germany.

Figure 14: MST analysis of HTRA1:CG, HTRA1:TT and HTRA1:CC. TC-tagged HTRA1 isoforms were heterologously expressed in Hek293 cells. TC-tagged HTRA1 was labeled with a FlAsH-EDT2, and subjected to MST analyses. Electrophoretic mobility of the fluorescent protein was assessed at increasing temperatures (from 32°C to 52°C). Supernatant of cells transfected with an empty vector were used for normalization. Data represent the mean +/- standard deviation (SD) of three independent experiments. The MST analyses were done by Dr. Thomas Schubert, 2bind GmbH, Regensburg, Germany.

MST analyses revealed a similar thermal migration behavior of the HTRA1 isoforms

HTRA1:CG and HTRA1:CC. In contrast, HTRA1:TT exhibited a significantly different

thermophoretic mobility suggesting an influence of AMD-associated polymorphisms

rs1049331:T and rs2293870:T on the tertiary structure of the protein (Figure 14).

4.3. HTRA1:CG, HTRA1:TT and HTRA1:CC protein conformation

comparison by limited partial proteolysis

In an independent approach, structural differences of the three protein isoforms were

assessed by partial proteolysis. This assay is sensitive to detect minor alterations in

protein structure by measuring the susceptibility of the protein substrate for proteases

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such as Trypsin (Park and Marqusee, 2005; Kimchi-Sarfaty et al., 2007; Kim et al.,

2009; Na and Park, 2009).

Figure 15: Partial proteolysis of recombinant HTRA1:CG, HTRA1:TT and HTRA1:CC with Trypsin. Strep-tagged HTRA1 isoforms were purified after heterologous expression in Hek293 cells. 1 µg of purified HTRA1 was incubated for 10 min at 37, 42 and 46 °C, respectively. Proteolysis was performed at 37°C for 5 min with 120 µg/ml Trypsin. Samples were subjected to Western blot analysis with α-HTRA1 antibodies. (A) shows a representative immunoblot. (B) shows densitometric analysis of immunoblots of three independent experiments from (A). HTRA1 signals were calibrated with measurements for HTRA1:CG after preincubation at 37°C. Data represent the mean +/- SD. Asterisks mark significant (*P < 0.05) and highly significant differences (**P < 0.01) between relative amounts of protein for HTRA1:CG or HTRA1:CC and HTRA1:TT.

Figure 15A shows that HTRA1:TT was more susceptible to trypsin digestion than

HTRA1:CG or HTRA1:CC under partial denaturing conditions. A strong decrease of

HTRA1:TT was observed after preincubation at 42°C, and after preincubation at

46°C, HTRA1:TT was completely digested. In contrast, no significant reduction of

HTRA1:CG and HTRA1:CC, subjected to these conditions, was observed. Figure

15B shows that the differences in protein levels between HTRA1:CG or HTRA1:CC

and HTRA1:TT were statistically significant after preincubation at 42°C (P<0.05), and

highly significant after preincubation at 46°C (P<0.01). Autoproteolysis of HTRA1 was

negligible. Together, the results from MST analyses and from partial proteolysis

suggest that HTRA1:CG and HTRA1:CC exhibit a similar tertiary structure that is

different from HTRA1:TT. With a further focus on the functional relevance of the

altered structure of HTRA1:TT, its properties were compared with that of the HTRA1

isoform translated from the reference haplotype, namely HTRA1:CG.

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4.4. Influence of synonymous SNPs within HTRA1 exon 1 on protein

secretion

Secreted proteins are subjected to ER quality control which retains and eventually

degrades misfolded protein species with a high sensitivity towards minor structural

alterations (Ruggiano et al., 2014). Secretion of HTRA1:CG and HTRA1:TT in

Hek293 cells was therefore monitored. Supernatant and cells were collected at

various time points between 0 h and 24 h after transfection with expression

constructs for HTRA1:CG and HTRA1:TT (Figure 16).

Figure 16: Influence of synonymous polymophisms on secretion of HTRA1 analyzed by immunoblot. After transfection of Hek293 cells with expression constructs for HTRA1:CG, HTRA1:TT or control, cells and cell culture medium (supernatant) were harvested at the indicated time points (0 to 24 h) and subjected to Western blot analyses with α-HTRA1 antibodies. The ACTB immunoblot served as loading control (A). Densitometric quantification of immunoblots of three independent repetitions of experiments from focusing on (B) intracellular or (C) extracellular HTRA1 protein shows HTRA1 signals were normalized against ACTB and calibrated

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against measurements for HTRA1:CG at 24 h. Data represent the mean +/- SD. Asterisks indicate statistically significant (*P < 0.05) and highly significant differences (**P < 0.01) between relative amount of protein for HTRA1:CG and HTRA1:TT.

Western blot analyses were performed with α-HTRA1 antibody. At 0 h, no HTRA1 is

detected. There is a strong increase in HTRA1 expression from 16 to 24 h. However,

relative to HTRA1:CG, intracellular amounts of HTRA1:TT were increased with a

statistically significant difference at 24 h (P < 0.05) (Figure 16A and B). Contrary to

this, a statistically significant reduction of HTRA1:TT protein relative to HTRA1:CG

was detected in the supernatant at 20 h and 24 h (Figure 16A and C). At time points

20 h and 24 h, the differences in amounts of extracellular protein of HTRA1:CG and

HTRA1:TT were statistically significant (P < 0.05) (Figure 16C).

Cumulatively, these data suggest a delayed secretion of HTRA1:TT relative to

HTRA1:CG.

4.5. Influence of synonymous SNPs within HTRA1 exon 1 on its

substrate affinity

4.5.1. Interaction of HTRA1 isoforms with TGF-β and β-casein analyzed

by MST

Several studies demonstrated a inhibiting influence of HTRA1 on TGF-β signaling,

although the type of interaction between the two molecules remained debatable (Oka

et al., 2004; Shiga et al., 2011; Zhang et al., 2012; Graham et al., 2013; Karring et al.,

2013). To investigate the interplay between HTRA1 and TGF-β, thermophoretic

mobility of TC-tagged HTRA1 dependent on increasing TGF-β1 concentrations was

measured using MST analyses. In order to label TC-tagged HTRA1 isoforms with

FlAsH-EDT2 dye, supernatants of Hek293 cells transfected with expression

constructs for TC-tagged HTRA1:CG and HTRA1 TT were incubated with 5 µM

FlAsH-EDT2. 100 nM of each labeled HTRA1 isoform was incubated with increasing

concentration of purified TGF-β1 ranging from 0.122 nM to 4000 nM or with β-casein

(from 1,22 nM to 40 µM). At a constant temperature of 25°C, at 80% MST power and

20% LED power, the binding affinity of fluorescently labeled HTRA1:CG and

HTRA1:TT with TGF-β1 was recorded on a Monolith NT.115.

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Figure 17: HTRA1 and its interaction with TGF-β1 and β-casein as measured by MST. Fluorescence-labeled HTRA1 (100 nM) was incubated with increasing concentrations of (A) TGF-β1 (0.122 nM to 4000 nM) or (B) β-casein (1.22 nM to 40,000 nM). Protein-protein interactions for HTRA1 and TGF-β1 were quantified by MST and binding data were plotted applying the Hill equation. The recorded fluorescence was normalized to the fraction bound (0 = unbound, 1 = bound). Data represent the mean +/- SD of three independent experiments. MST analyses were performed by Dr. Thomas Schubert, 2bind GmbH, Regensburg, Germany. The analyses of the MST interaction of HTRA1 isoforms and TGF-β1 were performed

by Dr. Thomas Schubert, 2bind GmbH, Regensburg, Germany. For HTRA1:CG in the

presence of mature TGF-β1, a typical sigmoidal binding curve with a binding affinity

of 63.2 +/- 8.8 nM was observed (Figure 17A). These results support a direct

interaction between HTRA1 and mature TGF-β. In comparison, HTRA1:TT isoform

failed to interact with TGF-β1 (Figure 17A). In contrast, titration of HTRA1 with its

substrate β-casein revealed a similar sigmoidal binding curve for both HTRA1

isoforms, HTRA1:CG and HTRA1:TT. The binding affinities were 527.9 +/- 96.4 nM

for HTRA1:CG, and 410.3 +/- 77.4 nM for HTRA1:TT, with the differences in binding

affinities not being statistically significant (Figure 17B).

4.5.2. Proteolytic cleavage of TGF-β and β-casein by different HTRA1

isoforms

Next, the proteolytic capacity of HTRA1:CG and HTRA1:TT for TGF-β and β-casein,

was analyzed respectively. Supernatants of Hek293 cells transfected with expression

vectors for HTRA1:CG, HTRA1:TT and control (empty expression vector) were

harvested. HTRA1 protein concentration in supernatant containing each HTRA1

isoform was 15 ng/µl. 4.5 µg of HTRA1 (in 300 µl supernatant) was incubated with 1

µg of TGF-β1 for 24 h and 20 µg of β-casein for 3 h. Same volume of supernatants

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transfected with empty expression vector were incubated with TGF-β1 and β-casein

similarly. This served as control. After 0, 4, 8, 16 and 24 h, samples were collected

from each mixture of TGF-β1 incubated with HTRA1:CG, HTRA1:TT and control.

Samples for casein-containing mixtures were collected at 0, 0.5, 1 and 3 h. The

casein in vitro digestion and the densitometry analysis of three independent

experiments was performed by Karolina Ploessl, Institute of Human Genetics,

University Hospital Regensburg, Regensburg, Germany.

Co-incubation of HTRA1:CG with mature TGF-β1 resulted in a gradual digestion of

TGF-β1. However, TGF-β1 cleavage by HTRA1:TT was strongly reduced compared

to HTRA1:CG (Figure 18A). Densitometry of three independent experiments

revealed that approximately 85% of TGF-β1 was cleaved by HTRA1:CG after 4 h,

whereas only 10% of TGF-β1 cleavage was observed in the presence of HTRA1:TT.

After 16 h, TGF-β1 was completely cleaved by HTRA1:CG, while at this time point

almost 60% of mature TGF-β1 was still present when incubated with HTRA1:TT

(Figure 18B). The observed differences for TGF-β1 cleavage by HTRA1:CG and

HTRA1:TT were statistically highly significant (P < 0.01).

Figure 18: Proteolysis of TGF-β1 and β-casein by HTRA1:CG, HTRA1:TT or control. (A) 1 µg of TGF-β1 or (C) 20 µg of casein were mixed with 300 µl serum-free medium of transfected Hek293 cells, containing 15 ng/µl of each HTRA1 isoform and incubated at 37°C. As a control, supernatant of Hek293 cells transfected with the empty expression vector was used. Samples were taken at the indicated time points.

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TGF-β1 proteolysis was captured via Western blot analyses with α-TGF-β antibody; casein digest was followed by Coomassie staining. Casein digest was conducted by Karolina Ploessl, Institute of Human Genetics, University Hospital Regensburg, Regensburg, Germany. Three independent experiments as described in (A) and (B) were performed and subjected to densitometric analysis for TGF-β1 (B) or β-casein (D). TGF-β1 or β-casein signals were calibrated against measurements for HTRA1:CG at 0 h. Data represent the mean +/- SD. Asterisks indicate statistically highly significant differences (**P<0.01) between TGF-β1 cleavage by HTRA1:CG and HTRA1:TT, respectively. Densitometry analysis for casein digest was performed by Karolina Ploessl, Institute of Human Genetics, University Hospital Regensburg, Regensburg, Germany.

In contrast to the effect of synonymous polymorphisms on TGF-β1 cleavage, both

HTRA1 isoforms exhibited similar proteolytic activities for the β-casein substrate

(Figure 18C and D).

4.6. Effect of HTRA1:CG and HTRA1:TT on TGF-β signaling

The different affinities of HTRA1:CG and HTRA1:TT for TGF-β indicate that the two

variants might also exhibit different effects on TGF-β-induced signal pathways.

Therefore, the effect of HTRA1:CG and HTRA1:TT on TGF-β signaling was

investigated.

4.6.1. Effect of HTRA1:CG and HTRA1:TT on TGF-β1-induced PAI-1

promoter activity in MLEC-PAI/Luc cells.

A reporter assay based on MLEC-PAI/Luc cells was used to analyze the effect of

HTRA1 on TGF-β1 signaling. These cells are stably transfected with a luciferase-

coding sequence under the control of the TGF-β-responsive element of the PAI-1

promoter, and thus, respond to stimulation by TGF-β family members (e.g. TGF-β1)

with heterologous luciferase expression (Abe et al., 1994). MLEC-PAI/Luc cells were

treated with 2 ng/ml of TGF-β1 in presence of Strep-tagged HTRA1:CG and

HTRA1:TT, or control protein (purified from supernatant of empty Strep-vector

transfected cells) for 16 h.

Addition of HTRA1:CG to MLEC-PAI/Luc cells resulted in a strong reduction of TGF-

β1-activated luciferase expression (15.6 +/- 2.8% luciferase activity compared to

control cells). HTRA1:TT was also capable of inhibiting TGF-β1-activated luciferase

expression, but only at 64.6 +/- 21.1% (Figure 19). The difference in luciferase

expression between cells treated with HTRA1:CG and HTRA1:TT was statistically

highly significant (P < 0.01).

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Figure 19: Effect of HTRA1:CG and HTRA1:TT on TGF-β1-induced PAI-1 promoter activity in MLEC-PAI/Luc cells. MLEC-PAI/Luc cells were stimulated with 2 ng/ml TGF-β1 in the presence of 40 ng/ml HTRA1:CG, or HTRA1:TT purified from supernatants of Hek293 cells transfected with expression constructs for Strep-tagged HTRA1 variants, or control protein (purified from supernatant of empty Strep-vector-transfected cells). After 16 h, luciferase activity was measured in triplicate wells each in three independent experiments. Measurements for each experiment were calibrated against the control (TGF-β1 + control protein). The mean +/- SD for the three independent experiments is given for each treatment. Asterisks indicate statistically highly significant differences (**P < 0.01).

4.6.2. Effect of HTRA1:CG and HTRA1:TT on SMAD phosphorylation

TGF-β signaling has been reported to be an important regulator of microglial

development and activation (Suzumura et al., 1993; Paglinawan et al., 2003; Li et al.,

2008; Butovsky et al., 2014). Specifically, extracellular binding of TGF-β family

members by TGF-β receptors lead to phosphorylation of SMAD proteins (Javelaud

and Mauviel, 2004a, b, 2005), important mediators of TGF-β-induced signaling

cascades. Therefore, the influence of HTRA1:CG and HTRA1:TT on the

phosphorylation of SMAD2 in BV-2 micoglial cells was analyzed. Autocrine SMAD

phosphorylation was triggered in BV-2 cells by adding BV-2-conditioned medium to

serum-starved BV-2 cells as described in Spittau et. al (2013). BV-2 cells (in

conditioned medium) were treated for 2 h with 40 ng/ml of Strep-tagged HTRA1:CG,

HTRA1:TT or control protein. The control protein was purified from supernatant of

cells transfected with empty Strep-vector (pEXPR-IBA103). Immunocytochemistry of

the treated cells with antibody against pSMAD2 was carried out by Magdalena

Schneider, Institute of Human Anatomy and Embryology, Faculty of Biology and

Preclinical Medicine, University of Regensburg, Germany (Figure 20A).

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Figure 20: Effect of HTRA1 variants on SMAD-phosphorylation analyzed via immunocytochemistry. BV-2 cells (in conditioned medium) were treated for 2 h with protein purified from supernatant of cells transfected with empty Strep-vector (control) (Left); HTRA1:CG (Centre) and HTRA1:TT (Right). Subsequently, the cells were subjected to immunocytochemistry with antibodies against pSMAD2 (performed by Ms. Magdalena Schneider, Institute of Human Anatomy and Embryology, Faculty of Biology and Preclinical Medicine, University of Regensburg, Germany) (A) or to immunoblot analyses using antibodies against pSMAD2, SMAD2 and ACTB (B). (C) Densitometry quantification of immunoblots was analyzed from (B) with three independent experiments. Signals for pSMAD2 and SMAD2 were normalized against ACTB and calibrated against the control. Data represent the mean +/- SD. Asterisks indicate statistically highly significant differences (**P < 0.01).

Immunocytochemistry with control cells showed a strong immunoreactivity for

phosphorylated SMAD2 (pSMAD2) within BV-2 cells, indicating an activated TGF-β

signaling pathway by endogenously synthesized and secreted TGF-β proteins

derived from BV-2-conditioned medium (Figure 20A Left). This result is in full

agreement with data on autocrine TGF-β expression and signaling in microglial cells

(Spittau et al., 2013). BV-2 cells treated with HTRA1:CG exhibited a significantly

reduced signal intensity of pSMAD2 indicating an inhibiting effect of HTRA1:CG upon

TGF-β signaling (Figure 20A Centre). In contrast, staining for pSMAD2 in BV-2 cells

treated with HTRA1:TT had a similar intensity as in the control experiment (Figure

20A Right).

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To quantify the effects of HTRA1 isoforms on SMAD phosphorylation, Western blot

analyses were performed with protein isolated from BV-2 cells treated as described

above.

The data of immunoblot analyses show that SMAD2 phosphorylation is almost

completely absent in BV-2 cells treated with HTRA1:CG, while HTRA1:TT reduced

measurable pSMAD2 levels to 71.9 +/- 2.5% compared to control (20B and C). While

the amounts in SMAD2 protein were comparable, the observed differences in

phosphorylated pSMAD2 were statistically highly significant (P < 0.01) (Figure 20C).

4.6.3. Effect of HTRA1:CG and HTRA1:TT on relative Pai-1 gene

expression

PAI-1 is a prominent target gene of TGF-β signaling (Cao et al., 1995; Kutz et al.,

2001; Dong et al., 2002). Upon TGF-β stimulation, phosphorylated SMAD2 and

SMAD3 form a complex with SMAD4, which is then translocated into the nucleus.

This complex stimulates expression of TGF-β response genes (Javelaud and

Mauviel, 2004b, a, 2005). Autocrine TGF-β signaling was triggered in BV-2 cells by

adding BV-2-conditioned medium to serum-starved BV-2 cells as described in Spittau

et. al (2013). BV-2 cells in conditioned medium were treated with 40 ng/ml Strep-

tagged HTRA1:CG and HTRA1:TT for 3 h and 24 h.

Figure 21: Effect of HTRA1:CG and HTRA1:TT on relative Pai-1 gene expression in BV-2 cells. BV-2 cells were treated for 3 and 24 h with recombinant HTRA1:CG, HTRA1:TT or control protein. PAI-1 mRNA expression was determined via qRT-PCR by Ms. Magdalena Schneider and Dr. Rudolf Fuchshofer, Institute of Human Anatomy and Embryology, Faculty of Biology and Preclinical Medicine, University of Regensburg, Germany. Experiments were performed in triplicates in four independent experiments. Results were normalized to the transcript levels of a mouse housekeeper gene, GNB2L and calibrated with the control at 3 h and 24 h, respectively. The mean +/- SD for the four independent experiments are

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given. Asterisks indicate statistically significant (*P < 0.05) and highly significant differences (**P < 0.01).

After 3h and 24 h respectively, RNA was isolated from the treated cells and cDNA

was synthesized. The relative Pai-1 gene expression was analyzed via qRT PCR by

Magdalena Schneider and Dr. Rudolf Fuchshofer, Institute of Human Anatomy and

Embryology, Faculty of Biology and Preclinical Medicine, University of Regensburg,

Germany (Figure 21). A strong downregulation of Pai-1 mRNA expression was noted

in BV-2 cells treated with HTRA1:CG when compared to control cells (12.5 +/- 12.9%

after 3 h; 14.5 +/- 13.9% after 24 h). In contrast, a less prominent decrease of Pai-1

transcripts was found after treatment with HTRA1:TT (74.0 +/- 35.5% after 3 h; 56.8

+/- 29.6% after 24 h, compared to control cells). The difference in Pai-1 expression

following treatment with HTRA1:CG or HTRA1:TT was statistically significant (P <

0.05) (Figure 21).

4.7. Effect of HTRA1 on microglial activation

Microglial activation and differentiation is strongly regulated by TGF-β signaling

(Rozovsky et al., 1998; Huang et al., 2010; Cekanaviciute et al., 2014; Norden et al.,

2014). As HTRA1 has been found to play a role in TGF-β signaling in microglial cells,

the effect of HTRA1 on microglial activation was investigated.

4.7.1. Effect of HTRA1 on classical activation of microglial (BV-2) cells via

LPS treatment

To activate classical (M1) microglial activation, 50 ng/ml of LPS was used in various

studies (Dirscherl et al., 2010; Karlstetter et al., 2014; Aslanidis et al., 2015). The

effect of HTRA1 on LPS-activated microglia was observed by treating the BV-2 cells

with 50 ng/ml of LPS in presence of increasing HTRA1 concentrations. A key

microglial enzyme induced in this process is the inducible nitric oxide synthase

(iNOS), which utilizes arginine to produce nitric oxide (Bagasra et al., 1995). Nitrite

production was thus measured as a marker for M1 activation. BV-2 cells, serum-

starved for 24 h, were treated with increasing concentrations of Strep-tagged HTRA1

(30, 60 and 90 ng/ml) in presence or absence of 50 ng/ml LPS.

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Figure 22: Effect of HTRA1 on nitrite production in BV-2 cells. BV-2 cells were serum-starved for 24 h before treatment. After 24 h, increasing concentration (30, 60, 90 ng/ml) of Strep-tagged HTRA1 was added to BV-2 cells in presence 0 or 50 ng/ml of LPS. After 24 h of treatment, nitrite production (in µM) was measured from the supernatant via NO assay. The mean +/- SD for the three independent experiments is given.

After 24 h, the NO assay revealed significant difference in nitrite production in non-

activated (1.2 +/- 0.2 µM) and LPS-activated BV-2 cells (20.7 +/- 0.4 µM). However,

there was no effect of HTRA1 on nitrite production by BV-2 cells in this in vitro assay

(Figure 22).

In the next step, the relative mRNA expressions of M1 markers, iNOS and IL6, were

analyzed by qRT-PCR. BV-2 cells were treated with LPS and HTRA1 as described

above. After 24 h RNA was isolated and gene expression of iNOS and IL6 was

analyzed via qRT-PCR.

Figure 23: Effect of HTRA1 on relative mRNA expression of M1 markers of BV-2 cells induced by LPS. BV-2 cells were serum-starved for 24 h before treatment. BV-2 cells were serum-starved for 24 h before treatment. After 24 h, increasing concentrations (30, 60 and 90 ng/ml) of Strep-tagged HTRA1 was added to BV-2 cells in presence of 0 or 50 ng/ml of LPS. After 24 h of treatment, the relative mRNA

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expressions of M1 markers, IL6 (A) and iNOS (B) were analyzed via qRT-PCR. Experiments were performed in triplicates in three independent experiments. Relative transcripts levels of IL6 and iNOS in BV-2 cells treated with 0 ng/ml of LPS and 0 ng/ml of HTRA1 served as control. Results were normalized to mouse ATPase transcript levels and calibrated with the control. The mean +/- SD for the three independent experiments are given.

The addition of 50 ng/ml of LPS induced the M1 activation markers iNOS and IL6 in

microglial cells. With the treatment of LPS, relative mRNA expressions of IL6 and

iNOS show fold changes of 203.2 +/- 17.7 and 1088.5 +/- 65.3, respectively,

compared to no LPS treatment. However, increasing concentrations of HTRA1 had

no effect on the expression of these markers (Figure 23 A and B).

4.7.2. Effect of HTRA1 on alternative activation of microglial (BV-2) cells

via IL4 and TGF-β1 treatment

IL4 is a well-described anti-inflammatory cytokine (Butovsky et al., 2005; Ledeboer et

al., 2000; Park et al., 2005; Zhao et al., 2006), which induces alternative (M2)

activation of microglia resulting in the expression of M2 markers Arginase 1 (Arg1)

and Chitinase-3-Like-3 (Chi3l3 or Ym1) in microglial cells (Gordon, 2003; Ponomarev

et al., 2007). TGF-β1 can enhance IL4-induced M2 activation of microglia by

increasing the expression of Arg1 and Ym1 either by a direct effect on Ym1/Arg1

promoter activity or indirectly by upregulating the IL4Rα, a receptor of IL4 (Zhou et

al., 2012).

Since HTRA1 plays a role in TGF-β signaling, role of HTRA1 in regulation of M2

microglial activation was investigated. Alternative activation was achieved via IL4 and

TGF-β1 treatment as described in Zhou et al. (2012). BV-2 cells, serum-starved for

24 h, were treated separately or in combination with 1 ng/ml of TGF-β1 and 10 ng/ml

of IL4 in presence of increasing Strep-tagged HTRA1 concentrations (30, 60, and 90

ng/ml).

Relative gene expression of both the M2 activation markers, Arg1 and Ym1 were

analyzed via qRT-PCR. The results indicate that upon treatment of TGF-β1 alone

there is no upregulation of the markers. In contrast, treatment of BV-2 cells with IL4

alone upregulates the M2 markers. Furthermore, when treated with IL4 and TGF-β1,

enhanced upregulation was observed in comparison to treatment with IL4 alone

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76 | P a g e

(Figure 24A and B). These findings are in full agreement with the previous study by

Zhou et al. (2012).

Treatment of BV-2 cells with HTRA1 alone in increasing concentration shows no

effect on gene expression of M2 markers. Increasing concentrations of HTRA1, in

combination with TGF-β1, also does not show any effect on regulation of M2

markers. However, HTRA1 shows an inhibition of M2 activation markers induced by

IL4 alone and a combination of IL4 and TGF-β1 (Figure 24A and B).

Figure 24: Effect of HTRA1 on relative mRNA expression of M2 markers of BV-2 cells induced via IL4/ TGF-β1. Relative mRNA expressions of Arg1 (A) and Ym1 (B) were analyzed by performing qRT-PCR of cDNA from serum-starved BV-2 cells, treated separately or in combination with 1 ng/ml of TGF-β1 and 10 ng/ml of IL4 in presence of increasing HTRA1 concentrations (30 ng/ml, 60 ng/ml, and 90 ng/ml). BV-2 cells, treated with increasing concentrations of HTRA1, in absence of IL4 and TGF-β1 were termed as “control”. Experiments were performed in triplicates in three independent experiments. Firstly, results were normalized to mouse ATPase

RESULTS

77 | P a g e

transcript levels. Results were then calibrated against transcript levels of the M2 markers of BV-2 cells treated with 0 ng/ml of HTRA1, IL4 and TGF-β1 (A and B). Alternatively, results of each treatment (IL4 alone, TGF-β1 alone and IL4 along with TGF-β1) were separately calibrated with the respective transcript levels of the M2 markers of BV-2 cells treated with 0 ng/ml of HTRA1 (C and D). Mean +/- SD for the three independent experiments are shown. Asterisks indicate significant (*P < 0.05) and highly significant (**P < 0.01) differences.

More specifically, relative gene expression of Arg1 shows a downregulation by 79.0

+/- 4.5%, 31.7 +/- 6.9% and 19.1 +/- 9.8% upon the treatment of BV-2 cells with IL4 in

presence of 30, 60 and 90 ng/ml of HTRA1 compared to the treatment of BV-2 cells

with IL4 in absence of HTRA1. (Figure 24 C). Similarly, relative gene expression of

Ym1 gets reduced by 75.7+/- 13.7%, 41.7 +/- 11.8% and 18.8 +/- 8.4% upon

treatment of BV-2 cells with IL4 in presence of 30, 60 and 90 ng/ml of HTRA1,

compared to the treatment of BV-2 cells with IL4 in absence of HTRA1(Figure 24D).

The relative gene expression of Arg1 shows a downregulation by 43 +/- 3.6%, 18.8

+/- 5.4% and 4.47 +/- 1.2%, upon the treatment of BV-2 cells with IL4 and TGF-β1 in

presence of 30, 60 and 90 ng/ml of HTRA1 compared to the treatment of BV-2 cells

with IL4 in absence of HTRA1 (Figure 24C). Similarly, relative transcript levels of

Ym1 show a downregulation by 24 +/- 2.5%, 7.8 +/-2.1% and 1.7 +/- 0.3%, upon the

treatment of BV-2 cells with IL4 and TGF-β1 in presence of 30, 60 and 90 ng/ml of

HTRA1 compared to the treatment of BV-2 cells with IL4 and TGF-β1 in absence of

HTRA1 (Figure 24D).

DISCUSSION

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5. DISCUSSION

With around 14 million people affected worldwide, AMD is one of the most common

causes of irreversible blindness (Resnikoff et al., 2004). AMD is a multifactorial

disorder caused by both genetic and environmental influences. Polymorphisms in

HTRA1 and ARMS2 on chromosome 10q26 are strongly associated with AMD.

However, there are conflicting data regarding the involvement of these genes in the

pathogenesis of AMD (Yang et al., 2006, Dewan et al., 2006, Kanda et al., 2007,

Fritsche et al., 2008). In addition, there are speculations on an involvement of both

genes in AMD development. (Yang et al., 2010).

The involvement of HTRA1 in the development of AMD, through various signaling

pathways, has been investigated in several studies. It was shown that overexpression

of HTRA1 in a mouse model regulates angiogenesis through TGF-β signaling (Zhang

et al., 2010). Another recent study also found an involvement of HTRA1 in IGF-1

signaling. Two AMD-associated synonymous polymorphisms [(rs1049331 (c.102C>T)

and rs2293870 (c.108G>T)] located in exon 1 of the HTRA1 gene were shown to

directly influence the ability of HTRA1 to regulate insulin-like growth factor 1 (IGF1)

signaling (Jacobo et al., 2013). A strong association of AMD pathology with these two

variants located in exon 1 of the HTRA1 gene has been previously reported in

several independent case-control association studies (Deangelis et al., 2008; Fritsche

et al., 2008; Tam et al., 2008); however, the binding capacity of HTRA1 for IGF1 is

contentious (Eigenbrot et al., 2012).

5.1. Effect of synonymous polymorphisms within exon 1 of HTRA1

on its structure and secretion

In recent years, the influence of synonymous or silent mutations on protein folding

and function has attracted increasing attention (Hunt et al., 2014). These alterations

although not associated with changes in amino acid composition of the protein were

reported to cause mRNA instability, exon skipping or alterations in co-translational

protein folding (Duan and Antezana, 2003; Duan et al., 2003; Chamary and Hurst,

2005; Chamary et al., 2006; Nackley et al., 2006; Kimchi-Sarfaty et al., 2007;

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Chamary and Hurst, 2009; Zhang et al., 2010; Brest et al., 2011; Lampson et al.,

2013). As a consequence, synonymous alterations in protein-coding sequences may

causally be involved in human pathologies. For example, synonymous changes in the

multidrug resistance 1 (MDR1) gene were shown to cause an altered drug and

inhibitor interaction leading to major changes in protein stability and transporter

specificity (Kimchi-Sarfaty et al., 2007; Fung et al., 2014). A schizophrenia-associated

synonymous SNP in the dopamine receptor D2 gene (DRD2) was also reported to

alter the rate of translation (Duan et al., 2003).

The first aim of the study was focused on the impact of three synonymous SNPs

(rs1049331:C>T, rs2293870:G>T and rs2293870:G>C) on conformation of HTRA1.

While rs1049331:C>T and rs2293870:G>T have been found to be associated with

AMD, rs2293870:G>C despite being on the identical nucleotide position of

rs2293870:G>T has not been found to be associated with the disease (Deangelis et

al., 2008; Fritsche et al., 2008; Tam et al., 2008; Jacobo et al., 2013; Friedrich et al.,

2015). Hence, three expression constructs for HTRA1 were generated respectively

including HTRA1 exon 1 variants rs1049331:C and rs2293870:G (referred to as

HTRA1:CG), the AMD-associated variants rs1049331:T and rs2293870:T

(HTRA1:TT), and the non-AMD-associated variants rs1049331:C and rs2293870:C

(HTRA1:CC). Thermophoretic movement of the three different isoforms was analyzed

by MST. This technique is based on thermophoresis, the directed motion of

molecules in temperature gradients. Thermophoresis is highly sensitive to all types of

binding-induced changes of molecular properties, be it in size, charge, hydration shell

or conformation (Seidel et al., 2013). It has recently been demonstrated that MST is

well-suited to follow protein unfolding and to detect unfolding intermediates, and that

it rivals more traditional approaches such as circular dichroism or tryptophan

fluorescence measurements in this capacity due to its speed and low sample

consumption (Alexander et al., 2013). Because of its high sensitivity to

conformational change, MST qualifies as a good technique to study the

conformational change caused by synonymous polymorphisms. In the present study,

thermophoretic movement of HTRA1 were significantly altered for the protein isoform

HTRA:TT, but not for the two other isoforms, HTRA1:CG or HTRA1:CC. MST results

were confirmed by performing limited proteolysis assay, which is an established

DISCUSSION

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technique for protein conformation studies (Varne et al., 2002; Huang, 2003; Fontana

et al., 2004). These assays documented an altered accessability of HTRA1:TT

isoform compared to HTRA1:CG isoform, clearly indicating structural differences of

both isoforms. This is in full agreement with findings first reported by Jacobo et al.

(Jacobo et al., 2013). In their work, the authors observed a decreased translation rate

of the isoform HTRA1:TT, which they ascribed to differential codon usage (Jacobo et

al., 2013). Synonymous codons are distinguished by the abundance of their

corresponding tRNA, which is lower for rare codons than for the frequent codons

(Cannarozzi et al., 2010; Tuller et al., 2010). Folding of nascent polypeptides occurs

cotranslationally (Frydman et al., 1994; Makeyev et al., 1996) and is influenced by the

rate of ribosome transit through the mRNA template. Therefore, codon frequency can

influence the rate of translation. The hypothesis that the attainment of a

conformationally immature protein with functional deficits is caused by the decreased

protein translation owing to common-to-rare codon conversion is consistent with

several publications (Zhang et al., 1998; Komar et al., 1999; Kimchi-Sarfaty et al.,

2007; Bartoszewski et al., 2010).

Secreted proteins undergo stringent ER quality control (Ruggiano et al., 2014).

Synonymous SNPs in Cystic Fibrosis Transmembrane Region (CFTR) causes local

misfolding of the protein and retention of the protein in ER for a prolonged time

(Zhang et al., 1998; Bartoszewski et al., 2010). That’s why time-dependent secretion

was monitored to compare the secretory property of the two conformationally different

HTRA1 isoforms. The results indicated that HTRA1:TT had a decreased secretion as

well as intracellular aggregation when compared to HTRA1:CG. Whether delayed

secretion or intracellular aggregation of HTRA1 or both could affect the progression

of AMD is yet to be clarified.

5.2. Effect of synonymous polymorphisms within exon 1 of HTRA1

on its substrate specificity

As a conformational change in the AMD risk-associated isoform was suggested, the

impact of the changes on substrate specificity of HTRA1 was the next the thing to be

investigated in our study. HTRA1 is a serine protease having an array of substrates.

HTRA1 digests major components of cartilage, such as aggrecan, decorin,

DISCUSSION

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fibromodulin and soluble type II collagen (Tsuchiya et al., 2005). Various other

substrates of HTRA1 that are associated with the complement system (clusterin,

vitronectin and fibromodulin) were found in supernatants of primary RPE cells (An et

al., 2010). An et al. (2010) additionally confirmed the proteolytic breakdown of

clusterin by recombinant HTRA1 via in vitro digestion. The pro form of complement

factor D (CFD) is cleaved by HTRA1 to activate CFD (Stanton et al., 2011). In

addition to these complement proteins, An et al.(2010) also demonstrated proteins

involved in the amyloid deposition (clusterin, alpha-2 macroglobulin and ADAM9) as

interaction partners of HTRA1. Amyloid is a component of drusen and it could be

shown that HTRA1 co-localized with so-called amyloid plaques in the brain of

Alzheimer's disease patients (Grau et al., 2005; Cameron et al., 2007). Grau et al.

(2005) identified the amyloid precursor protein C99 as substrate of HTRA1 by

showing its cleavage by recombinant HTRA1 in vitro. Many ECM-associated

substrates that are cleaved by HTRA1 such aggrecan, biglycan, decorin,

fibromodulin, fibronectin, soluble type II collagen and elastin were identified (Tsuchiya

et al., 2005; Chamberland et al., 2009; Jones et al., 2011). The study by Vierkotten et

al. (2011) was able to demonstrate that fibronectin, fibulin 5 and nidogen 1 are

digested in vitro by recombinant HTRA1, whereas, laminin and collagen type IV were

not digested by recombinant HTRA1 in vitro (Vierkotten et al., 2011). Jacobo et al.

(2013) also reported differential affinities of AMD-associated HTRA1 (HTRA1:TT)

variant towards IGF-1 interaction. However, the interaction between HTRA1 and IGF-

1 is challenged by findings from Eigenbrot et al. (2012) who failed to obtain any

evidence for binding of IGF-I or IGF-II, or insulin-binding activity by the N-domain of

HTRA1. This is further supported by a biosensor binding study which found no

appreciable Mac-25 interaction of HTRA1 with IGF-I or IGF-II (Vorwerk et al., 2002).

Among all the substrates of HTRA1, interaction with family of TGF-β proteins is the

most widely studied (Oka et al., 2004; Launay et al., 2008; Shiga et al., 2011; Zhang

et al., 2012; Graham et al., 2013; Karring et al., 2013). In several studies, HTRA1 has

been shown to inhibit TGF-β signaling but the exact mechanism of the inhibition

remains controversial. Shiga et al. (2011) describes that HTRA1 binds to pro-TGF-β

and cleaves it intracellularly, more precisely in the endoplasmic reticulum (ER). The

cleavage thereby inhibits TGF-β signaling. Oka and colleagues (2004), however,

DISCUSSION

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showed an extracellular digestion of the secreted active TGF-β by HTRA1. In another

independent study, in vitro digestion of TGF-β by HTRA1 was confirmed (Launay et

al., 2008). In an HTRA1-deficient mouse model, increased mRNA expression of

GDF6, a TGF-β family member, was observed. This mouse model showed increased

TGF-β signaling and increased vascularization of the retina (Zhang et al., 2012). It

has recently been reported that overexpression of HTRA1 causes reduced levels of

TGF-β receptors type II and III: HTRA1 was found to digest the TGF-β receptors type

II and III in vitro, but not the receptor type I or TGF-β (ligand) (Graham et al., 2013).

All these contradictory studies show that it is still under discussion, how HTRA1

inhibits TGF-β signaling (Oka et al., 2004; Shiga et al., 2011; Graham et al., 2013).

Since the TGF-β signaling is also closely linked to the VEGF-A signaling (Clifford et

al., 2008), regulation of TGF-β by HTRA1 could possibly also affect angiogenesis in

the eye influencing the progress of CNV.

In this work, an interaction between HTRA1 and TGF-β1 was investigated using MST

and in vitro proteolysis assay. MST has been successfully applied to study the

protein-protein interaction in a variety of studies (Arbel et al., 2012; Lin et al., 2012;

Wilson et al., 2012; Keren-Kaplan et al., 2013; Xiong et al., 2013; Zillner et al., 2013).

While a typical sigmoidal binding curve was observed in MST analyses of direct

interaction between HTRA1:CG and TGF-β1, no stable interaction between

HTRA1:TT and TGF-β1 was observed. In addition, evidence for a direct proteolytic

activity of isoform HTRA1:CG towards mature TGF-β1 was obtained via in vitro

digestion assay. In contrast, the in vitro digestion assay suggested a strongly

decreased turnover rate of TGF-β1 by HTRA1:TT compared with HTRA1:CG. The

apparent discrepancy raises the question why there is still an interaction of TGF-β1

and HTRA1 in the proteolysis assay, whereas at the same time, no interaction of the

two proteins is apparent in the MST assay. It is hypothesized that a stable interaction

between HTRA1 and TGF-β1 is measured by MST, resulting in a binding constant

whereas the proteolytic assay measures enzyme kinetics over a longer period of

time. A stable interaction between protease and substrate is not indispensible for

proteolytic cleavage, as described, for example, in rhomboid proteases (Dickey et al.,

2013). Although no physiological affinity of these proteases for their substrates were

displayed, an approximate 10,000-fold difference in proteolytic efficiency with

DISCUSSION

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substrate mutants and diverse rhomboid proteases were reflected in kcat values

alone.

5.3. Effect of HTRA1 variants on TGF-β signaling

As the AMD-associated HTRA1 isoform exhibited decreased affinity for TGF-β1, the

next step was to investigate whether this also affects TGF-β-mediated signaling

pathways. Reporter assay using MLEC-PAI/Luc is a widely used bio-assay for

quantification of TGF-β signaling (Abe et al., 1994; Shibuya et al., 2006; Zhou et al.,

2012). It was observed that the differential effect of HTRA1 isoforms on proteolysis of

TGF-β1 subsequently affected its regulation of TGF-β signaling, adressed in reporter

assays assessing the PAI-1 promoter, a prominent target of the TGF-β cascade.

Hara et al. (2009) reported similar consequences due to CARASIL-associated

HTRA1 variants. Specifically, two nonsense and one missense mutation in HTRA1

resulted in protein products that failed to repress signaling by the TGF-β family (Hara

et al., 2009). As increased TGF-β levels were observed in cerebral arteries of

CARASIL patients, the authors concluded that HTRA1 contributes to the pathogenic

processes of CARASIL via its control of TGF-β signaling. In this context, it is

interesting to note that increased TGF-β levels are also observed in AMD patients

(Guymer et al., 2011; Bai et al., 2014) and aberrant TGF-β signaling was connected

to AMD-related phenotypes in mice (Lyzogubov et al., 2011; Promsote et al., 2014)

and RPE cells (Yu et al., 2009a; Yu et al., 2009b; Vidro-Kotchan et al., 2011;

Promsote et al., 2014). This and our findings could imply a role for HTRA1 in AMD

pathogenesis via its regulation of the TGF-β pathway.

Our results also demonstrated control of HTRA1 over TGF-β regulation in cultured

microglial cells. Microglia are the major resident inflammatory cells of the central

nervous system and obtain key functions of immune surveillance and tissue repair

(Kreutzberg, 1996; Tambuyzer et al., 2009; Hughes, 2012). Thus, this cell type is

deeply involved in the pathogenic processes of various neurodegenerative diseases

like Parkinson’s disease (Schapansky et al., 2015), Alzheimer’s disease (Bamberger

and Landreth, 2002; D'Andrea et al., 2004; Streit et al., 2008; Latta et al., 2015; Lue

et al., 2015), Amyotrophic lateral sclerosis (ALS) (Boillee et al., 2006; Henkel et al.,

2009; Brites and Vaz, 2014), inherited retinopathies (Langmann, 2007; Karlstetter et

DISCUSSION

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al., 2010) or AMD (Ma et al., 2009; Damani et al., 2011; Ma et al., 2013; Wong, 2013;

Karlstetter and Langmann, 2014).

Aging is associated with increased numbers of retinal microglia in mice, and along

with tissue stress or injury, aging causes retinal microglia to undergo phenotypical

changes whereby they become larger and less dendritic, which are typical

morphological signs of activation (Xu et al., 2009). While microglia are generally

believed to return to their ramified phenotype and leave the subretinal space once the

injury has subsided, the response of aging microglia to injury is slower and less

reversible (Langmann, 2007). Moreover, during an insult or injury, microglia migrate

from the inner retina to the subretinal space, which is normally devoid of microglia.

The importance of subretinal accumulation of activated microglia in AMD is not clear,

but could be both a symptom of inflammatory damage and a beneficial response to

injury, since infiltration of microglia/macrophages to sites of retinal injury may

promote neovascularization, while impairment of this accumulation in the subretinal

space exacerbates retinal degeneration (Sakurai et al., 2003; Ma et al., 2009; Ambati

et al., 2013). Another study reported the role of microglia in the local control of

complement activation in the retina and presented the age-related accumulation of

ocular lipofuscin in subretinal microglia as a cellular mechanism capable of driving

outer retinal immune dysregulation in AMD pathogenesis (Ma et al., 2013). Microglial

cells express VEGF receptors and eyes treated with anti-VEGF showed a

significantly decreased number of activated microglia in the retina and the choroid.

This suggests that the beneficial effects of anti-VEGF therapy in wet AMD may

exceed its vascular effects (Couturier et al., 2014).

Activation and differentiation of microglia is strongly regulated by TGF-β signaling

(Rozovsky et al., 1998; Huang et al., 2010; Cekanaviciute et al., 2014; Norden et al.,

2014). TGF-β is a pleiotropic cytokine that has inflammatory and anti-inflammatory

activities depending on the cellular environment of the innate immune cells [reviewed

in (Letterio and Roberts, 1998; Wan and Flavell, 2007)]. The maintenance of the

normal retinal immune regulation seems to actively involve TGF-β from the RPE.

TGF-β contributes to the immune privilege by predisposing microglia to the

preferential production of IL10, which in turn downregulates antigen-presenting

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molecules including MHC-II, CD80 and CD86 (D'Orazio and Niederkorn, 1998). TGF-

β also directs TNF/IFN-γ-stimulated microglia cells to an anti-inflammatory phenotype

by broadly blocking inflammatory gene expression (Paglinawan et al., 2003).

Therefore, our next aim was to compare the effect of HTRA1 variants on regulation of

TGF-β signaling in BV-2 microglial cells. The results showed that phosphorylation of

SMAD2 and Pai-1 expression, both markers for activated TGF-β signaling (Cao et al.,

1995; Kutz et al., 2001; Dong et al., 2002; Javelaud and Mauviel, 2004a, b, 2005),

were significantly lower in microglial (BV-2) cells treated with HTRA1. Thus, our data

further provide evidence for a role of HTRA1 in the differentiation and activation of

microglia, and therefore likely in the pathogenesis of neurodegenerative diseases.

Notably, the inhibitory effect of HTRA1:TT on microglial TGF-β signaling was weaker

compared to the reference HTRA1 isoform, HTRA1:CG. In a mouse model of another

adult-onset neurodegenerative disease, ALS, TGF-β1 was found to inhibit the

neuroprotective response by microglia and T cells. A TGF-β signaling inhibitor slowed

the disease progression and extended survival of the mice (Endo et al., 2015). In a

mouse model of Alzheimer’s disease, blockade of TGF-β–SMAD2/3 signaling in

innate immune cells is shown to reduce senile plaque formation in the brain

parenchyma (Town et al., 2008). So, it can be speculated that the altered regulation

of TGF-β signaling in AMD might enhance the pathogenesis of AMD.

5.4. Effect of HTRA1 on classical and alternative microglial

activation

To give first insights into a possible involvement of HTRA1 in microglial activation, the

effect of HTRA1 on classical (M1) and alternative (M2) activation of microglial cells

was included in this study. Microglia can be activated by the cytokines interferon-γ

(IFN-γ), interleukin-17 (IL17) or LPS into a proinflammatory phenotype (M1), whereas

IL4 or IL13 induce a state of anti-inflammatory phenotype (M2), which is associated

with neuroprotective functions that promote repair (Butovsky et al., 2006b;

Ponomarev et al., 2007; Kawanokuchi et al., 2008). IL4 and IL13 suppress the

production of pro-inflammatory cytokines such as IL8, IL6 and tumor necrosis factor-α

(TNF-α), and reduce nitrite release, which collectively protect against LPS/IFN-γ-

induced M1 microglial activation in vitro and in vivo (Ledeboer et al., 2000; Butovsky

DISCUSSION

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et al., 2005; Park et al., 2005; Zhao et al., 2006). Counteracting pro-inflammatory

cytokines is accompanied by the induction of typical M2 markers, such as Arginase 1

(Arg1), Chitinase-3-Like-3 (Chi3l3 or Ym1), Mannose receptor (CD206) and Found in

inflammatory zone 1 (FIZZ1) (Gordon, 2003; Ponomarev et al., 2007). Anti-

inflammatory factors are also released by M2 microglia when apoptotic cells or myelin

debris are removed (Liu et al., 2006). Neurotrophic factors, such as insulin-like

growth factor 1 (IGF-I) are also released by M2 microglia to assist inflammation

resolution and promote neuron survival (Suh et al., 2013). It is postulated by many

studies that acquired deactivation by anti-inflammatory cytokines such as IL10 and

TGF-β is another state of M2 activation to alleviate acute inflammation and is induced

primarily by uptake of apoptotic cells or exposure to insult or injury (Qin et al., 2006;

Colton, 2009; Colton and Wilcock, 2010). Notably, the neuroprotective signals of

alternative activation and acquired deactivation orchestrate each other in a

coordinated way against the proinflammation responses. For example, TGF-β can

enhance IL4-induced M2 microglia by increasing the expression of Arg1 and Ym1.

Exogenous TGF-β1 enhances IL4-induced Ym1 and Arg1 expression either by a

direct effect on Ym1/Arg1 promoter activity or indirectly by upregulating the IL4Rα,

receptor of IL4, through activation of the MAP kinase pathway. Furthermore,

treatment with IL4 shows increase in the expression and secretion of endogenous

TGF-β2. Autocrine TGF-β2 in turn might be able to enhance IL4-induced Arg1

expression similar to exogenous TGF-β1 (Zhou et al., 2012).

No effect of HTRA1 on 50 ng/ml LPS-induced M1 microglial cells was detected in our

study. However, the possibility of any effect of HTRA1 on M1 activation cannot be

ruled out. 50 ng/ml LPS is a very high stimulus for classical activation, giving almost

maximal activation. Weak effects by HTRA1 could simply be superimposed by the

strong stimulus. Therefore, it is needed to be confirmed whether HTRA1 has an effect

on M1 markers induced by a lower dose of LPS. Furthermore, it has been proposed

that LPS interferes with key components in the TGF-β1 signaling pathway in primary

microglia. The study showed that the ability of TGF-β1 to exert anti-inflammatory

effects is significantly reduced by LPS leading to prolonged survival of M1 microglia,

although the exact mechanism remains to be clarified (Mitchell et al., 2014). As

HTRA1 has been shown to have an interaction with TGF-β1 and plays a role in TGF-

DISCUSSION

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β1 signaling, to investigate an effect of HTRA1 in regulation of TGF-β1 signaling in

LPS-stimulated microglia would be interesting.

Next, the effect of HTRA1 on M2 microglial activation was investigated. HTRA1 has

been shown to downregulate induced alternative activation (M2) markers of BV-2

cells, Arg1 and Ym1 induced by IL4 alone and IL4 in combination with TGF-β1. The

downregulation of M2 microglial activation induced by a combination of IL4 and TGF-

β1 can be explained by the capacity of HTRA1 to cleave TGF-β1 (as shown earlier).

Interestingly, HTRA1 also downregulates expression of Arg1 and Ym1 induced by IL4

alone. As mentioned before Zhou et al. (2012) demonstrated that IL4-induced Arg1 is

also dependent on autocrine TGF-β2 signaling. Our results could therefore be

explained by HTRA1 cleaving the endogenously produced and secreted TGF-β or

TGF-β receptors type II and III, as suggested by Graham et al. (2013).

Even though this study is a preliminary study, it is first of its kind to show a novel role

of HTRA1 in regulation of M2 microglial activation. Nevertheless, further read-outs

like Arginase assay or studies on microglial morphology are needed to confirm these

first results. It also requires more precise understanding of the exact mechanism of

action of HTRA1 in downregulating IL4-induced alternative activation. It is to be noted

that here only non-AMD-associated HTRA1 (HTRA1:CG) isoform was used for

treating the BV-2 microglial cells. To connect this study with reference to AMD

pathogenesis, the foremost requirement would be to include AMD-associated HTRA1

isoform (HTRA1:TT) to compare the effects of both HTRA1 isoforms on the activation

markers. Till date, two independently studied HTRA1 transgenic mice models showed

AMD-like phenotypes (Jones et al., 2011; Vierkotten et al., 2011). More detailed

studies of the microglia of the HTRA1 transgenic mice may unravel a novel role of

HTRA1 in AMD pathogenesis from an immunological point of view.

CONCLUSION

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6. CONCLUSION

Taken together, our data show that HTRA1 is an important regulator of TGF-β

signaling by direct interaction and cleavage of TGF-β. AMD-associated

polymorphisms in HTRA1 [rs1049331 (c.102C>T) and rs2293870 (c.108G>T)] result

in delayed protein secretion, reduced TGF-β1 affinity, and decreased capacity to

control TGF-β signaling and autocrine TGF-β regulation in microglia. One might be

tempted to speculate a functional contribution of AMD-associated polymorphisms in

HTRA1 to AMD pathogenesis, via their effect on the affinity of HTRA1 for TGF-β.

Nevertheless, other so-far-unknown sites of interaction at which HTRA1 antagonizes

the TGF-β pathway (or might influence cellular processes involved in AMD

pathogenesis) cannot be excluded. Moreover, it has also been shown that gene

expression of HTRA1 is highly heterogeneous among different individuals,

independent of their HTRA1 genotype (Friedrich et al., 2011), with expression levels

of HTRA1 varying up to 20-fold. In principle, this questions an involvement of reduced

HTRA1 activity on AMD development. In addition, due to high LD within the

chromosomal HTRA1 gene locus, a number of additional polymorphisms reveal an

equally strong association with AMD extending to and encompassing variants at the

neighboring ARMS2 gene. The final decision about which gene, HTRA1 or ARMS2,

is causally involved in AMD pathogenesis requires further scrutiny and specifically

needs the understanding of functional aspects of the as-of-yet unknown ARMS2

protein.

LIST OF FIGURES

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LIST OF FIGURES

Figure 1: Anatomy of retina.. ....................................................................................... 4

Figure 2: Pathological hallmarks of early AMD and late AMD revealed by fundus

photography. ................................................................................................................ 6

Figure 3: Schematic representation of three common phases of microglial activity in

the retina...................................................................................................................... 9

Figure 4: Schematic overview of the AMD-associated 23.3 kb region on chromosome

10q26 exhibiting high linkage equilibrium. ................................................................. 13

Figure 5: Nineteen common AMD risk variants in the discovery study of the AMD

Gene Consortium meta-analysis.. ............................................................................. 15

Figure 6: Structure of HTRA1. ................................................................................... 16

Figure 7: Three-dimensional structure of HTRA1 trimer.. .......................................... 17

Figure 8: Schematic overview of inducing autocrine TGF-β/SMAD signaling in BV-2

cells.. ......................................................................................................................... 54

Figure 9: Schematic diagram of relative positions of synonymous polymorphisms in

exon 1 of HTRA1. ...................................................................................................... 58

Figure 10: Schematic representation of expression constructs for (A) untagged,

(B)TC-tagged and (C)Strep-tagged HTRA1 variants. ................................................ 59

Figure 11: Characterization of HTRA1 expression and bioactivity. Hek293 cells were

transfected with expression constructs for HTRA1:CG and HTRA1:TT variants.. ..... 60

Figure 12: Adjustment of HTRA1 concentrations in supernatant of Hek293 cells

transfected with expression vectors containing TC-tagged HTRA1 variants.. ........... 61

Figure 13: Labeling TC-tagged HTRA1 with FlAsH-EDT2 for MST analyses. ........... 62

Figure 14: MST analysis of HTRA1:CG, HTRA1:TT and HTRA1:CC... ..................... 63

Figure 15: Partial proteolysis of recombinant HTRA1:CG, HTRA1:TT and HTRA1:CC

with Trypsin.. ............................................................................................................. 64

Figure 16: Influence of synonymous polymophisms on secretion of HTRA1 analyzed

by immunoblot. .......................................................................................................... 65

LIST OF FIGURES

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Figure 17: HTRA1 and its interaction with TGF-β1 and β-casein as measured by

MST.. ......................................................................................................................... 67

Figure 18: Proteolysis of TGF-β1 and β-casein by HTRA1:CG, HTRA1:TT or control..

.................................................................................................................................. 68

Figure 19: Effect of HTRA1:CG and HTRA1:TT on TGF-β1-induced PAI-1 promoter

activity in MLEC-PAI/Luc cells.. ................................................................................. 70

Figure 20: Effect of HTRA1 variants on SMAD-phosphorylation analyzed via

immunocytochemistry.. .............................................................................................. 71

Figure 21: Effect of HTRA1:CG and HTRA1:TT on relative Pai-1 gene expression in

BV-2 cells.. ................................................................................................................ 72

Figure 22: Effect of HTRA1 on nitrite production in BV-2 cells. ................................. 74

Figure 23: Effect of HTRA1 on relative mRNA expression of M1 markers of BV-2 cells

induced by LPS. ........................................................................................................ 74

Figure 24: Effect of HTRA1 on relative mRNA expression of M2 markers of BV-2 cells

induced via IL4/ TGF-β1.. .......................................................................................... 76

LIST OF TABLES

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LIST OF TABLES

Table 1: Stages of AMD divided into AREDS categories according to characteristics 6

Table 2: Chemicals and reagents .............................................................................. 20

Table 3: Kits and ready-made solutions .................................................................... 22

Table 4: Buffers and solutions ................................................................................... 24

Table 5: E.coli strains ................................................................................................ 26

Table 6: Mammalian cell lines ................................................................................... 26

Table 7: Cell culture media and supplements ............................................................ 27

Table 8: Medium for cultivation of E.coli .................................................................... 27

Table 9: Enzymes ...................................................................................................... 27

Table 10: Primary antibodies (mAB: monoclonal antibody; pAB: polyclonal antibody;

WB: Western blot) ..................................................................................................... 28

Table 11: Secondary antibodies ................................................................................ 29

Table 12: Starting vectors .......................................................................................... 29

Table 13: Control plasmids and plasmids with insert already available ..................... 29

Table 14: Sequence and use of oligonucleotides for PCR in the study ..................... 30

Table 15: Primers for first strand cDNA synthesis ..................................................... 31

Table 16: Probes and oligonucleotides used for qRT-PCR ....................................... 31

Table 17: Consumables ............................................................................................. 32

Table 18: Instruments ................................................................................................ 33

Table 19: Software tools ............................................................................................ 36

Table 20: Cultivation of mammalian cells .................................................................. 37

ABBREVIATIONS

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ABBREVIATIONS

Short Form Long Form

µ Micro

µl Microliter

µm Micrometer

µM Micromolar

AMD Age-related Macular Degeneration

Ampr Ampicillin resistant

APS Ammonium Per Sulphate

ATP Adenosine Triphosphate

BrM Bruch’s Membrane

cDNA Complementary DNA

CFH Complement Factor H

CNV Choroidal Neovascularization

DMEM Dulbecco’s Modified Eagle Medium

DMSO Dimethyl sulfoxide

DNA Deoxyribonucleic Acid

dNTP Deoxy nucleotide Triphosphate

E.coli Escherichia coli

ECL Enhanced Chemiluminescence

ECM Extracellular Matrix

EDTA Ethylendiamintetraacetate

ER Endoplasmic Reticulum

Et al. Et aliter (and others)

FBS Fetal Bovine Serum

GA Geographic Atrophy

h Hour/s

ABBREVIATIONS

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H2O Water

H2O2 Hydrogen peroxide

IB Immunoblot

Kb Kilobasepair

L Liter

KDa Kilo Dalton

LB Liquid broth

LD Linkage Disequilibrium

LPS Lipopolysaccharide

Min Minute/s

ml Milliliter

MLEC Mink Lung Endothelial Cells

MM Master mix

mM Millimolar

nM Nano molar

OD Optical Density

ON Overnight

PBS Phosphate buffered saline

PCR Polymerase Chain Reaction

Pen/Strep Penicillin/Streptomycin

PVDF Polyvinylidenfluoride

qRT-PCR Quantitative Real-Time PCR

RNA Ribonucleic Acid

RPE Retinal Pigment Epithelium

RPM Rotations Per Minute

RPMI Roswell Park Memorial Institute

RT Room temperature

S Second/s

SDS-PAGE Sodium Dodecyl Sulphate-Polyacrylamide Gel

Electrophoresis

ABBREVIATIONS

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SNP Single Nucleotide Polymorphism

TBE Tris Borate EDTA

TC Tetra-cysteine

TEMED Tetramethylethylendiamine

TE Tris-EDTA

V Volt

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ACKNOWLEDGMENT

Firstly, I would like to express my sincere gratitude to my advisor Prof. Dr. Bernhard

Weber for the continuous support in my Ph.D. study and related research, for his

patience, motivation, and immense knowledge. His guidance helped me throughout

my Ph.D. and writing of this thesis. I could not have imagined having a better advisor

and mentor for my Ph.D. study.

I cannot thank Dr. Ulrike Friedrich enough for her support and love throughout the

period of my Ph.D. Her relentless support and suggestions on a regular basis were

indispensable.

Besides my advisor, I would like to thank the rest of my mentors: Prof. Dr. Gunter

Meister and Dr. Sascha Fauser not only for their insightful comments and

encouragement, but also for the critical questions which helped me to widen my

research from various perspectives. I would like to thank Dr. Heidi Stoehr, Dr.

Helmut Roth, Dr. Diana Pauly, Prof. Dr. Thomas Langmann and Prof. Dr. Antje

Grosche for their valuable inputs about my research during the lab meetings.

My sincere thanks also go to Prof. Gernot Laengst and Dr. Rudolf Fuchshofer for a

successful collaboration. Without their precious support it would not be possible to

conduct this research. I would specially want to thank Dr. Thomas Schubert and

Magdalena Schneider for their co-operation.

I thank my fellow labmates and interns for the stimulating discussions when we were

working together, before deadlines, and for all the fun we have had in the last four

years. I specially want to thank Dr. Felix Grassman, Karolina Ploessl, Kerstin Rueckl,

Kerstin Meier, Andrea Milenkovic for their valuable suggestions and assistance. I

also want to thank my colleagues in Diagnostics: Katja, Julia, Yvone, Chris, Merve.

Last but not the least, I would like to thank my family: my parents and my wife for

supporting me throughout the writing of my thesis.

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